Review: Health Care CAD Systems for Breast Microcalcification Cluster Detection

Transcription

1 Journal of Medical and Biological Engineering, 32(3): Review: Health Care CAD Systems for Breast Microcalcification Cluster Detection Maria Rizzi * Matteo D Aloia Beniamino Castagnolo Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Bari 70126, Italy Received 22 Jul 2011; Accepted 17 Feb 2012; doi: /jmbe.980 Abstract Of all the known cancers, breast cancer is the most widespread cancerous pathology among women. As causes of its onset are till unknown, there are no effective ways to prevent breast cancer. An efficient diagnosis in its early stage can thus give women a better chance of full recovery. Therefore, early detection of breast cancer is the key for reducing the associated morbidity and mortality rates. Mammography is the most effective and reliable method for breast cancer early detection. It is a radiological screening technique which makes the detection of breast lesions possible using low radiation doses. It allows breast cancer diagnosis at a very early stage when disease treatment by suitable therapies for the pathology regression/cure is still effective. In a widespread screening program, it is difficult for radiologists to provide both accurate and uniform evaluation particularly because of the large number of mammograms to be analyzed. Therefore, computer-aided detection (CAD) systems, which automatically detect signs of illness in its early stage, are important and necessary for breast cancer control. They provide a second opinion to help physicians detect abnormalities. Microcalcifications and masses are the two most important indicators of illness malignancy, and their automated detection is very valuable for early breast cancer diagnosis. The high correlation between the appearance of microcalcification clusters and diseases suggests that CAD systems for the automated detection of microcalcification clusters can be very useful and helpful for avoiding misdiagnosis and for early stage cancer detection. The present study summarizes the various methods adopted for microcalcification cluster detection and compares their performance. Moreover, reasons for the adoption of a common public image database as a test bench for CAD systems, motivations for further CAD tool improvements, and the effectiveness of various CAD systems in a clinical environment are given. Keywords: Computer-aided detection (CAD), Microcalcifications, Breast cancer, Mammography, Receiver operating characteristic (ROC) 1. Introduction Advancements in technology and medical science have led significant innovations that greatly improve human health and quality of life. Health technology is used to avoid illness onset, reduce the risk of occurrence, and limit impact. It helps clinicians to screen abnormalities and it contributes to the diagnosis of clinical signs for recognizing the nature and cause of pathological events. Moreover, technology is expected to reduce mortality and morbidity rates, to shorten illness duration, to improve the quality of care (also increasing access to it), to reduce the relapse risk, and to limit the decay of a person activities/functionalities which corresponds to an increase in life expectancy [1]. Biomedical engineering has played a very important role in health care technological development. It adopts engineering * Corresponding author: Maria Rizzi Tel: ; Fax: rizzi@de .poliba.it principles and techniques to solve problems in biological and medical environments [2-5]. Biomedical engineering provides tools and methods for improving health care delivery in both diagnosis and disease treatments. These tools include instrumentation, medical imaging, computer-aided detection and diagnosis and medical devices. In particular, computer-aided detection (CAD) systems have become important tools in supporting physicians for neoplastic pathology detection and prevention, especially when diagnostic images are difficult to analyze due to low quality and a high number of examinations have to be analyzed. With the spread of nationwide screening programs for the early detection of breast cancer, the amount of mammograms to be analyzed by radiologists has greatly increased. Therefore, the risk that radiologists may miss some subtle abnormalities exists, mostly due to images often having poor contrast and showing different features depending on breast anatomy and tissue density, which changes with patient age and hormonal/physical conditions. In cases of dubious diagnosis, the behaviour of radiologists tends to be fairly cautious in order to avoid misdiagnosing malignant cases, with patients referred

2 148 J. Med. Biol. Eng., Vol. 32 No for further invasive diagnostic procedures such as biopsy. The drawback of such an approach is a high number of non-productive biopsy examinations with high economic costs. Statistics show that only 15-34% of breast biopsies are proved cancerous and that 10-30% of all cases of breast cancer go undetected by mammography [6]. The diagnosis sensitivity (i.e., accuracy in recognizing all malignant pathologies) and specificity (i.e., possibility of classifying benign pathologies as malignant) may be improved by having each mammogram checked by two radiologists. However, this would make the process inefficient and affect the management of diagnostic institutes. An efficient alternative is to replace one of the radiologists by a computer system. Therefore, the aim of CAD systems is to improve physician performance by prompting sites of potential abnormalities and to reduce the number of missed lesions. CAD can thus improve the early diagnosis of breast cancer by providing quantitative analysis of specific regions. The CAD system normally operates as an automated second opinion or as a double reading system that indicates lesion locations and types of possible abnormalities [7-9]. Mammograms are examined for the presence of malignant masses, skin thickening, and microcalcifications. Masses often occur in breast-tissue-dense areas and have many shapes, such as circumscribed, spiculated, lobulated, or ill defined. Circumscribed masses usually have distinct boundaries and high radiopaque density; spiculated masses have rough, star- shaped boundaries; lobulated masses have irregular shapes. Round, low-density masses with smooth, sharply defined margins are generally considered benign whereas high-density, stellate, spiculated masses with poorly defined margins are considered malignant [10]. Microcalcifications are tiny calcium deposits accumulated in breast tissue. Generally, they appear in mammograms as small bright spots embedded within an inhomogeneous background [11]. Size, shape, density, distribution pattern, and number of microcalcifications are analyzed in the benign and malignant classification phase [12]. Malignant micro- calcifications generally have a diameter of less than 0.5 mm and are fine, linear-branching, stellate-modeled, and varying in size and shape. Generally, their distribution pattern is clusters of more than 3 microcalcifications [13]. Although micro- calcifications have high inherent attenuation, their small size can make detection difficult, especially in poor-quality images. The difficulty increases in analyzing mammograms of young women, who have high-density breast tissue, for predominance of fibro glandular tissues. In a breast that is particularly dense, mammography sensitivity for early malignancy detection is reduced as a result of the effort required in locating cancer within an opaque, uniform background [14,15]. In these situations, the difficulty is mainly due to the reduced contrast between microcalcifications and surrounding dense tissues. A connection has been found between the presence of clustered microcalcifications and the occurrence of breast cancer, which implies that the early detection of microcalcifications in mammograms can increase the chances of survival for patients with breast cancer. The high correlation between the appearance of microcalcification clusters and breast cancer shows that automated microcalcification detection would be very helpful for breast cancer control [16]. Several CAD systems are commercially available, including R2 ImageChecker and icad Second Look [17,18]. The algorithms detect potential masses and microcalcification clusters, and the systems mark those in which a particularly high probability of malignancy is identified. Commercial systems do not claim perfect sensitivity and specificity, nor do they claim to detect all manifestations of cancer [17-19]. Generally, the techniques adopted in CAD systems have a major impact on performance. Although a lot of techniques have been proposed, the development of new algorithms for CAD of breast cancer is still an active research field, particularly in regard to the detection of subtle abnormalities in mammograms [9,20]. This survey reviews methods for suppressing noise, enhancing contrast between the region of interest (ROI) and background, extracting and selecting microcalcifications, and detecting and classifying microcalcification clusters. Moreover, the paper underlines the importance of adopting a common public image database as a test bench for CAD systems, motivations for further CAD tool developments, and improvements and the effectiveness of various CAD systems in clinical environments. 2. Breast cancer detection with mammography Mammography is characterized by a low radiation dose and is the only imaging method widely accepted for routine breast cancer screening [21-24]. Mammography allows radiologists to perform both screening and diagnostic examinations. Screening mammography is used to detect breast cancer in an asymptomatic population, whereas diagnostic mammography is used to examine a patient who has been found to have abnormal clinical elements, such as a breast mass or other disease signs or symptoms. Signs of breast cancer may include pain, skin thickening, nipple discharge, or a change in breast size or shape. Diagnostic mammography is often performed as a follow-up examination to abnormal screening mammograms [25]. The adoption of mammographic examinations, especially screening mammography, has been proven both to increase the cancer detection rate and to reduce morbidity and mortality rates [26]. Mammogram interpretation can be difficult because abnormalities and healthy tissue have poor contrast and the appearance of a normal breast differs for each woman. Moreover, mammograms may be compromised if there is powder or salve on breasts or if the patient has undergone breast surgery. Mammography thus has a high rate of false positives and false negatives. For this reason, some women without cancer undergo further clinical evaluations or breast biopsy, and others discover pathologies in a later stage, when medical treatments have a reduced probability of producing good results. To increase mammography accuracy and sensitivity, image analysis by two radiologists is infeasible due to economic costs [27,28]. The adoption of an automatic CAD

3 Breast microcalcification cluster CAD 149 system seems to be the best choice for improving the cancer detection rate in the early stage [29,30]. CAD systems generally have multiple phases, including image pre-processing (for noise suppression and image contrast enhancement), segmentation (for suspicious zone localization), feature extraction (for lesion classification and false positive reduction), and detection and classification (Fig. 1). Image under test Pre-processing phase Segmentation phase Feature extraction phase Microcalcification detection Classification phase Localized clusters Figure 1. Schematic representation of CAD system phases. 3. Mammogram image pre-processing phase Various methods have been proposed for the preprocessing phase, which aims to enhance the intensity contrast between target objects and the background and produce a reliable representation of breast tissue structures. Although microcalcifications are usually brighter than their surroundings, their contrast in a dense breast is quite low so that human eyes can hardly distinguish them. In this situation, the aim of the pre-processing phase is to increase the contrast of microcalcifications over a threshold to separate them from their surroundings. Many techniques can enhance an image without degrading it. Enhancement procedures can be grouped into those that use global, local, or multiscale processing. 3.1 Global processing approach Global approaches include contrast stretching and histogram equalization techniques. Contrast stretching, also called full-scale histogram stretching (FSHS), is a simple linear point operation that expands the image histogram to fill the whole available grayscale range. A well distributed histogram is characteristic of an image with high contrast and good detail visibility [31]. FSHS was applied in [32] to improve mammogram contrast and visibility, allowing for more accurate contours and feature detection. FSHS produces clear improvements in the visual quality of an image suffering from a limited distribution of gray levels. Histogram equalization (also called histogram flattening) is a nonlinear point operation that re-assigns pixel intensity values to obtain a uniform distribution of intensities over the full gray scale range. It increases the image contrast range by increasing the dynamic range of gray levels [33-35]. This technique is simple but effective if the analyzed image contains only one object which has low contrast with respect to the background. Improvements to the method include multi-peak histogram equalization, which determines the range of gray pixel levels by values of mean, median, or number of peaks in the histogram [36]. 3.2 Local processing approach Although global methods are simple, they do not take into account local details. In diagnostic medical images, local details may be more important than global contrast. Adaptive histogram equalization (AHE) [37], adaptive contrast enhancement (ACE) [38], and morphological contrast enhancement [39,40] are three well known local enhancement methods. In the AHE method, a contextual region is first defined and then a histogram of that region is obtained. The center of the rectangular region is then moved to the adjacent pixel and the histogram equalization is repeated. This method allows each pixel to adapt to its neighboring region, so that high contrast can be obtained for all locations in the image. ACE algorithms adopt unsharp masking techniques. The image under test is separated into two components: the lowfrequency unsharp mask obtained by low-pass filtering of the image, and the high-frequency component obtained by subtracting the unsharp mask from the original image. The high-frequency component is then amplified and added back to the unsharp mask to form an enhanced image. Morphological contrast enhancement is an image enhancement technique based on mathematical morphology operations. In morphology, an object in an image is considered as a set of points and operations are defined between two sets: the object and the structuring element (SE). A common technique for contrast enhancement is the combined use of the top-hat and bottom-hat transforms. The top-hat transform is defined as the difference between the original image and its opening. The opening of an image is the collection of foreground parts of an image that fit a particular SE. Therefore, the brightest spots in the original image are highlighted using this transformation. The bottom-hat transform is defined as the difference between the closing of the original image and the original image. The closing of an image is the collection of background parts of an image that fit a particular SE. Therefore, the darkest areas in the original image are highlighted using this technique. 3.3 Multiscale processing approach Numerous enhancement methods based on the wavelet transform have been proposed for microcalcification detection [41-46]. Due to its multi-resolution properties, the wavelet transform is basically a filtering technique that represents images hierarchically on the basis of scale or resolution,

4 150 J. Med. Biol. Eng., Vol. 32 No analyzing high-spatial-frequency phenomena localized in space. In mammograms, microcalcifications, masses, and background can be selectively enhanced, detected, or reduced within different resolution levels as they appear at different scales. In general, the digitalized mammogram is transformed using a wavelet technique and coefficients are modified to enhance microcalcification features. The final image is obtained using the inverse wavelet transform. The various methods differ in their choices of mother wavelet and coefficient modification method. 4. Mammogram image segmentation phase After the pre-processing phase, the separation of suspect areas containing microcalcifications from the background parenchyma is performed. This step characterizes the sensitivity of the CAD system. Several segmentation methods have been proposed. Many researchers adopt global/local thresholding. The global thresholding technique, based on global information such as the histogram [47], is useful for segmentation as microcalcifications appear brighter than surrounding tissues. Regions including abnormalities give rise to histograms with extra peaks whereas healthy regions have only a single peak. After the application of global thresholding, objects can be separated from the background. Local thresholding can be used to refine the obtained results because the threshold value is determined locally for each pixel, referring to intensity values of surrounding pixels [48]. Region growing is another adopted segmentation technique. This method groups pixels with properties similar to those of a seed pixel. Once the growing region algorithm is terminated, if the average intensity of the grown region is much greater than the surrounding region, then the pixel is classified as a pixel belonging to a microcalcification. Every pixel in the image is chosen successively as a seed pixel and the above procedure is repeated [49]. Due to the contrast present in mammographic images, fuzzy logic has been introduced for suspicious object segmentation [50]. The algorithm first assigns a fuzzy membership value to each pixel and then an error value is calculated. Fuzzy membership is updated taking into account neighboring pixel effects. The algorithm stops when a zero error is reached, indicating that each pixel has been assigned either to a bright (microcalcification) or dark (background) region. In [51], the sub-segmentation of mammograms was performed using the possibilistic fuzzy c-means (PFCM) clustering algorithm. In this procedure, pixels with similar gray intensity levels are found and grouped together into a determined number of classes or objects. The subsegmentation PFCM consists of finding, within clusters localized using the segmentation process, data less representative (or atypical data) belonging to clusters. They represent zones of interest during the image analysis. In particular, typicality values are used and each group is divided in two sub-groups: a sub-group of typical data (or data with typicality values greater than a specified threshold) and a sub-group of atypical data (or data with typicality values below the established threshold). Morphological operators have been combined with the wavelet transform [52] and Otsu s method [48] for microcalcification segmentation. In [53], the former technique was applied to enhance image contrast between microcalcifications and the background, while the latter procedure automatically selects the best threshold to segment microcalcifications in a region of interest. In post-processing, another set of morphological filters is applied to fill up any flaw in the segmented microcalcifications. Multiscale techniques have also been applied for suspicious area segmentation. An adaptive and multiscale processing method for segmentation was presented in [54]. It adopts a discrete wavelet transform for feature decomposition and multiscale representation. In [55], a two-stage method, based on wavelet transforms for microcalcification detection and segmentation, was proposed. Resulting sub-bands are thresholded to find microcalcifications and detected pixel sites are dilated for image enhancement. By weighting sub-band details at detected pixel sites and by subsequently computing the inverse wavelet transform, a reasonable segmentation of microcalcification outlines is obtained, so that suspect objects are greatly enhanced in the output image. The continuous wavelet transform was used in [56] in the implementation of a microcalcification segmentation method that adapts to both size and shape variations. Size is estimated by a polar-transformed active contour model (called active rays) on a continuous wavelet representation and shape adaptivity is achieved by a subsequent region growing step. The procedure obtains contour point estimates by implementing active rays on an analytic scale-space representation in a coarse-to-fine strategy. A region growing method is used to delineate the final microcalcification contour curve, with pixel aggregation constrained by the microcalcification contour point estimates. 5. Mammogram feature extraction phase In the feature extraction phase, abnormalities inside the mammogram under test are detected. To achieve this objective, a feature selection process is necessary. An optimum subset of features is selected from those available in a given problem domain after the image segmentation process. According to which features are selected, the feature space can be divided into sub-spaces such as intensity features, geometric features, and texture features. In [57], a two-level wavelet filter based on the Haar mother wavelet was implemented. The orthogonality of the adopted wavelet ensures that the information represented by the wavelet coefficients is irredundant, resulting in an efficient detection of microcalcifications. To detect suspected zones, a hard thresholding method is adopted which requires the mean pixel value and the standard deviation to be evaluated. The proposed procedure analyzes the subband images obtained for each decomposition level adopted for a nonlinear filter and checks for local maximum points, local minimum points, and zero-crossing points. As microcalcification size is rarely wider than 1 mm and

5 Breast microcalcification cluster CAD 151 the mean distance between two microcalcifications belonging to a given cluster is less than 5 mm, the adopted filter considers two detected maximum/ minimum/zero crossings to represent a single singularity point if they are less than 1 mm from each other. In this way, the number of false positives is reduced. The detected singularity points represent suspected zones (i.e., zones inside which microcalcifications are present and microcalcification clusters could exist). In order to find potential microcalcification clusters, a feed-forward artificial neural network (ANN) was used in [58]. Four features were selected for the classification process: minimum diameter, minimum radius, mean radius of the clusters, and number of microcalcifications. The maximum diameter is defined as the maximum distance between two microcalcifications in a cluster. The minimum diameter is the maximum distance between two microcalcifications within a cluster projected on the perpendicular to the maximum diameter. The minimum radius is the shortest radius connecting each microcalcification to the centroid of the cluster; and the mean radius is the mean of these radii. The method proposed in [59] is based on the mammogram Laplacian scale-space representation. First, possible microcalcification locations are identified as local maxima in the filtered image on a range of scales. For each finding, size and local contrast are estimated based on the Laplacian response. A finding is marked as a micro- calcification if the estimated contrast is larger than a predefined threshold, which depends on the finding size. In [60], the fractal dimension of a mammogram was calculated based on alternating sequential filters. The method extracts a four-dimensional feature vector using a Gabor filter bank. These features become input for a neural network. In [61], pixel intensity was evaluated; if it falls in a specific range, a region growing algorithm is applied and the intensity gradient is computed to test whether the candidate pixel satisfies mean and variance criteria. Two different feature extraction techniques are used in [62] which are based on first order and second order (co-occurrence matrix) approaches. The first-order features are based on the mammogram intensity histogram. Several useful parameters (features) are calculated in order to quantitatively describe the first-order statistical properties of the image. However, these features, applied locally, are not able to completely characterize the image texture. Therefore, the procedure uses a second-order histogram analysis based on the joint probability distribution of pixel pairs inside the image. Five first-order and six second-order features are obtained. The method presented in [63] adopts the gray-level cooccurrence matrix to extract features from a segmented mammogram. Fourteen features are obtained. To improve classification performance, a modified QuickReduct algorithm is used to reduce the feature set. The reduction of attributes is achieved by comparing equivalence relations generated by sets of attributes. Attributes are removed so that the reduced set provides the same predictive capability of the decision feature as that of the original. Two features are selected as a reduced set: the angular second moment and the correlation. 6. Mammogram classification phase Generally speaking, in CAD systems, the classification phase attributes a particular object (such as an abnormality) to a class. In CAD systems for microcalcification cluster detection, two classes are defined: the class of microcalcifications organized in a cluster structure (namely the cluster present class) and the class of microcalcifications without a cluster pattern (namely the cluster absent class). Therefore, after the classification phase, microcalcification clusters are detected and localized. Microcalcification cluster features, which have been extracted and selected, represent inputs for classifiers to distinguish detected suspicious areas as either "cluster present" or "cluster absent". Various classifiers have been proposed. (1) In [64], relevant vector machines (RVMs) were adopted. In particular, a two-stage classification approach was implemented. In the first stage, which comprised a linear RVM classifier, background pixels are eliminated, and in the second stage, a nonlinear RVM classifier is used to analyze pixels not eliminated in the first classification stage. (2) A mixed-featurebased neural network (MFNN) was implemented in [65]. The MFNN employs features computed in both spatial and spectral domains and uses spectral entropy as a decision parameter. Back-propagation with Kalman filtering is employed for efficient network training. (3) Image processing, pattern recognition, and artificial intelligence were combined in [66]. Mathematical morphology was used for image enhancement; a k-means algorithm was adopted to cluster the data based on feature vectors and a feed-forward neural network (FFNN) classifier was applied. An FFNN is a collection of mathematical models that imitate the properties of the biological nervous system and the functions of adaptive biological learning. It is made of many processing elements that are highly interconnected together with weighted links similar to synapses. The adopted FFNN, trained with the back-propagation algorithm, has four inputs, one hidden layer with nine units, and one output. The transfer function of every neuron is the sigmoid hyperbolic tangent function. (4) To generate a likelihood map of each mammogram, the radial basis function neural network (RBFNN) classifier was implemented in [67] with input image features extracted using Gabor filters. The pixel value in the likelihood map represents the possibility of that pixel being classified as a microcalcification pixel. (5) In [68], the number of microcalcifications within a region of fixed area was adopted for cluster localization. A square area of 1 cm 2 was used as discontinuity measure to distinguish a new cluster. 7. CAD performance evaluation Comparisons of CAD method performance are very difficult and even impossible due to the use of different databases for testing. A CAD method that yields very satisfactory results with a particular database might yield

6 152 J. Med. Biol. Eng., Vol. 32 No unsatisfactory performance with another. Table 1 shows several available mammogram databases. Mammographic Image Analysis Society (MIAS) Washington University Digital Mammography Database Digital Database for Screening Mammography (DDSM) Nijmegen Digital Mammogram Database UCSF/LLNL Digital Mammogram Library Table 1. Available mammogram databases An organization of UK research groups which has generated a digital mammogram database. The database contains 322 digitized films and includes radiologist opinions about locations of any abnormalities and their classification (malignant or not). This database contains digitally acquired images of proven breast pathology. Each case consists of a single scout image of a breast lesion obtained during the course of stereotactic core needle biopsy. Cases are organized according to lesion histopathology. Database produced as a collaboration between the Massachusetts General Hospital, the Sandia National Laboratories, and the University of South Florida Computer Science and Engineering Department. The database contains approximately 2,500 studies. Images containing suspicious areas have associated information about locations and types of suspicious region. Database is composed of 40 images provided by the Department of Radiology, University Hospital Nijmegen. All mammograms show one or more microcalcification clusters. Database consists of a 12-volume CD library of digitized mammograms featuring microcalcifications. It was developed by Lawrence Livermore National Laboratories (LLNL) and the University of California at San Fransisco (UCSF) Radiology Department. The performance of diagnostic systems is measured based on the percentage of diagnostic decisions proven to be correct. However, this parameter strongly depends on disease prevalence and it does not indicate false positive and false negative error rates. To overcome these limitations, sensitivity and specificity indices are adopted. Sensitivity is defined as the probability of detecting a microcalcification cluster where a cluster actually exists and specificity represents the probability of obtaining a negative mammogram when no microcalcification clusters exist. They are respectively computed as follows: Sensitivity (Se) = TP/(TP + FN) (1) Specificity (Sp) = TN/(TN + FP) (2) where: TP (number of true positives) is the number of correct identifications of microcalcification clusters inside the mammogram under test FN (number of false negatives) is the number of microcalcification clusters present in the image that the algorithm is unable to detect FP (number of false positives) is the number microcalcification clusters detected by the algorithm but really not present in the mammogram TN (number of true negatives) is the number of images that the procedure considers to have no microcalcification clusters that really have not microcalcifications High values of both parameters are desirable for CAD systems. However, in real situations, there is a trade-off between Sp and Se on the basis of the impact of FP and FN diagnoses and on the prevalence of disease in the subjects under test. For example, during the screening phase, a diagnostic system should ensure that most of the true positive cases are detected (high sensitivity) at the cost of some false positives. The Se and Sp values of a diagnostic system depend on the particular confidence threshold that the observer or the CAD system uses. To overcome this problem, the receiver operating characteristic (ROC) curve is used, which indicates the tradeoff between sensitivity and specificity and thus describes the system s inherent discrimination capacity. The curve is a plot of Se (Y axis) versus one minus Sp (X axis) for various threshold values (Fig. 2). The ROC analysis makes a pixel- based evaluation. When the pathological structure detection and its localization are both important, a region-based analysis is more convenient. The free-response ROC (FROC) curve is thus used in CAD algorithm evaluation and comparison (Fig. 3). FROC analysis is similar to the ROC plot except that the X axis represents the number of FPs/image. Figure 2. Example of a ROC curve obtained from a typical CAD system. Figure 3. FROC curve shape produced by the CAD system in [32]. The area under the ROC curve or the FROC curve (denoted as A Z ) is an important parameter for diagnostic performance evaluation. The A Z value of a ROC curve is just the area under the ROC curve whereas the A Z value of a FROC curve is evaluated by normalizing the area under the FROC curve by the range of the abscissa. An A Z value equal to 1.0 means that the diagnostic detection has perfect performance; that is, the T P rate and FP rate are equal to 100% and 0%, respectively. In order to compare some microcalcification cluster detection methods, the MIAS database was selected as a test bench. Table 2 summerizes the adopted procedure, the number of images used for the algorithm test, and the obtained results.

7 Breast microcalcification cluster CAD 153 Table 2. Comparison of recent microcalcification cluster detection methods that adopt the MIAS database as a test bench. Paper Brief Description Performance The algorithm locates microcalcification clusters by analyzing the distribution of Y. Jin et al. [48] brightness over digital mammograms. The method adopts the one-dimensional WT technique. The detection is achieved from the parent child relationship between the zero-crossings (Marr-Hildreth M.G. Mini et al.[44] (M-H) detector)/local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The procedure investigates the performance of statistical modeling of E. Regentova et al. [41] mammograms by means of wavelet-domain hidden Markov trees. The algorithm implements contrast-limited adaptive histogram equalization and E. Regentova et al. [44] morphological operations for breast localization inside mammograms and for artifact removal The method processes the breast by a wavelet-based filter. Three-layer A. Retico et al. [45] feed-forward NN used for feature classification. The tool detects MCs using WT. For breast localization, a skin-line segmentation G. Rezai-rad et al. [46] procedure is applied an ANN is adopted as a classifier. A morphological approach is first employed to isolate microcalcifications from L. Song et al. [52] the background, and then WT is applied for microcalcification detection. The algorithm adopts a wavelet filter for background noise removal and for S.N. Yu et al. [14] suspicious microcalcification preservation. To reduce the false positive rate, Markov random field parameters are used. The procedure adopts WT filters for removing background noise and recognizing M. Rizzi et al. [32] true microcalcifications. Isolated microcalcifications are discarded and clusters are localized. The procedure involves wavelet image de-noising and enhancement performed M. Rizzi et al. [58] by point processing operators. For microcalcification cluster detection, a back-propagation ANN technique is adopted. The algorithm decomposes the image using the integer wavelet transform and J. Dheeba et al. [67] Gabor features are extracted from the image. Se = 90,48% 1,2 Fp/im Se = 95% 0,6 FP/im (M-H); 0,55 FP/im (Canny) Se = 92,5% 2 Fp/im Se = 100% 2,9 FP/im Se = 88% 2,18 FP/im Se = 94% 1,12 FP/im Se = 80,2 2,5 FP/im Se = 92% 0,75 FP/im. Se = 98% 1 FP/im Se = 98% 0.65 FP/im. Se = 85.2% Number of MIAS mammograms tested Not Available Not Available Not Available 20 Whole database Whole database Whole database 8. CAD systems in clinical application and final remarks Generally speaking, radiology has become very important to medical diagnosis due to advances in image quality, detector systems, and computer technology. Accurate diagnosis and/or assessment depends on both image acquisition and image interpretation. Image interpretation by radiologists is affected mainly by the non-systematic search pattern of a typical human being, noise, and complex disease onset. CAD systems, considered as a second opinion in detecting pathologies, assessing extent of disease, and making diagnostic decisions, are expected to improve medical image interpretation. In particular, CAD systems for detecting abnormalities in mammograms (such as microcalcifications, masses, and architectural distortions) can play a key role in breast cancer early detection. For scientists, there are several interesting research topics in cancer detection systems, such as the development of highefficiency and high-accuracy lesion detection algorithms. Radiologists, on the other hand, are attracted by CAD system effectiveness in clinical applications. From a clinical point of view, the CAD system goal is not cancer detection but providing assistance to radiologists to avoid disregarding a lesion present in a mammogram., CAD systems should mark lesions missed by radiologists. From a practical viewpoint, if the computer misses too many lesions detected by radiologists, physicians will lose confidence in the system. In the case of microcalcification cluster detection, success depends on the following conditions: the CAD system is able to detect microcalcification clusters missed by radiologists; radiologists must be able to recognize when the CAD has detected a microcalcification cluster which has been missed by them; that is, they must be in a position to distinguish computer warnings for cluster true detections from those for false detections It is difficult to compare various CAD methods because different databases or criteria are used for evaluation. Database characteristics influence the training phase of a computer method (i.e., feature selection and classifier training) as well as performance. Databases can be described by objective measures, such as lesion size and contrast, and by subjective measures, such as lesion subtlety, which depends on the observer. The way a database is used also influences the development and the reported performance of a method. For instance, during the training and testing phases of a machine learning approach (such as ANN and SVM), it is important that multiple images of a given lesion (e.g., medio-lateral-oblique view and craniocaudal view of a given lesion) be used together either for the training or for the test process (i.e. it is necessary to avoid that some views of the same lesion are included in the training set and the others in the test set). A thourough literature review reveals that the DDSM and MIAS databases are the most commonly used public sources of mammograms (even if some researchers adopt private databases provided by hospitals). Because DDSM and MIAS usage is not standardized, studies rarely use exactly the same test cases. Moreover, most studies lack a detailed description of the selection criteria for cases or images. Besides, also after the

8 154 J. Med. Biol. Eng., Vol. 32 No study of these papers, the modality of image selection is not clearly deducible. In this situation, results reported by authors are difficult to verify and, consequently, performance comparisons with other studies are difficult to make. A large number of papers have proposed CAD methods that use image ROIs. Remaking the adopted ROI dataset is often impossible even if the selected images are indicated because no description has been given of the method used for creating the dataset. A standardized database of a large number of images which reflects realistic clinical situations is thus necessary. In particular, the database should include mammograms with either more than one lesion or various breast densities and degrees of suspicion. Images in the database should be homogeneous and have consistent accuracy. Lesions belonging to a given typology should be annotated in the same manner and images should show lesions in the same way. However, at present, in a given public database, some ROIs include the lesion and its surrounding tissue, whereas others include only the lesion or only part of the lesion which crosses the ROI margin. To help researchers, some sub-set, such as an ROI sub-set, composed of images extracted from the main mammogram database should be created. Then, a set of standard operating procedures explaining the methodology for the selection of the most suitable sub-database in dependence of the particular study to make should be created along with the database. Moreover, the database should contain the criteria for case inclusion or exclusion. Another factor that affects the reported performance of a CAD system is the way the sensitivity parameter is given (in terms of a percentage of detected lesions per image or per case). The acceptance of a CAD system in a diagnostic environment depends not only on the performance of the method alone, but also on: 1. how well a radiologist performs the task when the computer output is used as an aid; 2. the ability to integrate the procedure into routine clinical practice. Cross-sectional and longitudinal studies can be employed for measuring CAD effectiveness in clinical environments such as mammographic screening. Cross-sectional analysis needs a sequential data collection. A radiologist initially examines the mammogram under test and renders an opinion without help from CAD. Then, a physician looks over the computer results and either confirms or changes the previous opinion. Adopting this methodology, CAD effectiveness is determined patient by patient and the number of extra lesions detected as consequence of the CAD adoption can be computed. A longitudinal study is based on historical or temporal comparisons. The cancer detection rate can be compared between two time periods, one before CAD was implemented clinically and the other after CAD use. In this method, CAD effectiveness is determined from the change in the cancer detection rate. Changes in detection rate measured in cross-sectional and longitudinal studies should be interpreted differently. The cross-sectional method appears to be more consistent because it achieves the goal of CAD that is, the reduction of the radiologist missed cancer rate. In actual clinical studies, breast cancer prevalence is affected by the number of women with no previous screening, cancer growth rate, patient age, fraction of women with risk factors, frequency at which women are screened, and so on. Changes in prevalence affect longitudinal studies much more strongly because different populations are compared directly. For the evaluation of CAD, longitudinal studies (historical controls) are inherently different from cross-sectional studies (sequential reading) when the cancer detection rate is the aim of a screening program. It is important to understand this fundamental difference between the two types of clinical CAD study. Failure to do so makes it difficult to interpret the results of different studies, leading to confusion as to the real benefits of a CAD system [69]. Validation studies of CAD systems reported in literature are still rather primitive due to the lack of validation statistics that provide clinically relevant performance measures in breast cancer screening. However, mixed results on the role of current CAD systems in practical situations have been reported [70,71]. Some of them tend to overemphasize the sensitivity parameter (i.e., accuracy in recognizing all the malignant pathologies) at the expense of specificity (i.e., probability of classifying benign pathologies as malignant). This, in many cases, may result in prescribing further invasive diagnostic procedures such as biopsy. The drawback of such an approach is a high number of non-productive biopsy examinations which result in high economic costs for hospital institutions and psychological stress for patients. Therefore, a CAD workstation should be configured for each radiologist to allow individualized control over sensitivity and specificity of computer output with adjustments depending on the particular situation and personal preference. For example, a radiologist might prefer a computer output with high sensitivity for examining high-risk patients being screened for cancer, whereas a lower computer sensitivity and potentially a correspondingly higher specificity might be desirable for patients at low risk for cancer. 9. Conclusion Computer-aided detection and diagnosis systems have been developed to help radiologists avoid missing lesions and giving accurate diagnoses, acting as a second opinion. A lot of procedures for mass and microcalcification detection have been proposed. This paper reviewed the most used image processing techniques for microcalcification cluster detection. Many improvements have been implemented in CAD systems but their performance has yet to be optimized in terms of sensitivity and specificity. In clinical application, a CAD system with a low specificity value leads to many false positives with subsequent psychological stress experienced by patients. In contrast, a low sensitivity leads to false negatives, which produce false reassurance associated with cancer detection at a

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