A Novel Method For Automatic Screening Of Nonmass Lesions In Breast DCE-MRI

Transcription

1 Volume 3 Issue 2 October 2015 ISSN: International Journal of Informative & Futuristic Research A Novel Method For Automatic Screening Of Paper ID IJIFR/ V3/ E2/ 046 Page No Subject Area Key Words Computer Sci. & Engineering Ductal Carcinoma In Situ (DCIS), Computer-Aided Diagnosis, Nonmass Lesions, Breast MRI,DCE-MRI 1 st Anjali J. 2 nd Prof. Reji George M.Tech. Scholar Department of Computer Sci. & Engg., Musaliar College Of Engineering And Technology, Pathanamthitta - Kerala (India) Professor & Head, Department of Computer Sci. & Engg., Musaliar College Of Engineering And Technology, Pathanamthitta - Kerala (India) Abstract Mammograms and breast MRI studies were assessed independently by different radiologist. Small and nonmass-enhancing lesions are diagnostically challenging and easily missed in a routine clinical diagnosis. The early detection of breast cancers like Ductal Carcinoma In Situ(DCIS) is important, as it supports effective and minimally invasive treatments. The correct segmentation of the breast is often required as a fundamental step to facilitate next diagnostic tasks that is done by computer-aided diagnostic (CAD) tools. e.g., breast density measurement, lesion detection and is automatically reported. The aim of this work is to investigate the sensitivity with which ductal carcinoma in situ (DCIS) is diagnosed by mammography and by breast MRI. Correction of patient motion is a fundamental pre-processing step for dynamic contrast enhanced MRI(DCE-MRI),eliminating artifacts induced by involuntary movement and facilitating quantitative observation of contrast agent kinetics. Image registration algorithms often used for this task align subsequent temporary pictures of the dynamic MRI by increasing intensity, correlation or entropy-based similarity measures between image pairs. Here different works are compared to the accurate detection of nonmass lesions in breast DCE-MRI. Available online through - Accepted After Review On: October 23, 2015 Published On: October 29,

2 1. Introduction Mammograms can be either used to analyse breast cancer in women who have neither signs nor symptoms of the disease. This type of mammograms is called a diagnostic mammogram. Breast cancer is cancer that develops from breast tissue. Signs of breast cancer may include a lump in the breast, a change in breast shape, dimpling of the skin, fluid coming from the nipple, or a red scaly patch of skin. In those with distant spread of the disease, there may be bone pain, swollen lymph nodes, and shortness of breath or yellow skin. According to the BI-RADS lexicon, based on their morphological characteristics, the lesions are classified into mass, nonmass, and foci [1].The diagnosis of breast cancer in its earliest stage might help to prevent from growing to invasive cancers [2]. The presence of abnormal cells inside a milk duct in the breast is known as Ductal carcinoma in situ (DCIS). DCIS is considered as one of the earliest forms of breast cancer. DCIS is non-invasive which means that it has not spread out of the milk duct to invade other parts of the breast. The early and accurate detection of breast cancer which is not only improves the survival rate but also avoid unnecessary biopsies. Studies show that mammography can also be used for early detection and treatment of breast lesions [3] [4].However, interpretation of mammograms are subjective process, so inter observer variability is common. Hence, computer-aided diagnostic (CAD) tools are developed and studied in order to assist the radiologists in fairly accurate objective interpretation of mammograms [1,2,5].The application of classifiers in medical diagnosis are increasing gradually. There is no doubt that the evaluations of data s are taken from patients and the decisions of medical specialists are the most important factors in diagnosis. Classification systems [6] can help to minimize the possible errors and can also provide instant verification of medical data as soon as possible and in a slightly detailed manner. CAD systems combine a wide variety of techniques. CAD software help radiologists to detect better masses and micro calcifications in mammograms therefore can improve the accuracy of mammography and also can reduce the subjectivity associated with the manual interpretation process. In CAD software, the mammograms are first enhanced using standard image enhancement methods mainly to sharpen the boundaries of the region of interest (ROI) and to increase the contrast between the ROI and the nearby normal tissue. The ROIs are then segmented through common statistical, region-based, morphological approaches and significant features are extracted for subsequent clustering or classification. Automatic motion correction represents a major prerequisite to a correct automatic small lesion examination. Motion artifacts are caused either by the relaxation of the pectoral muscle or involuntary patient motion. Invalidate the assumption of the same spatial location within the breast of the nearby voxels in the acquired volumes for verifying lesion enhancement. Especially for small lesions, the assumption of correct spatial alignment frequently leads to misinterpretation of the diagnostic significance of enhancing lesions. Visual evaluation of morphologic characteristics is a highly inter-observer variable, while automated verification of features leads to more reproducible indices. 2. Literature Survey In this literature survey different motion correction, segmentation, feature extraction and classification methods are described. Image registration techniques are employed for this purpose which combines a local elastic and global affine transformations into one deformation field [7].The combined algorithm takes the advantages of both elastic and linear affine transformation methods. 566

3 A specific segmentation of the breast is the fundamental step to facilitate after diagnostic tasks. Lei Wang previously implemented a fully automatic segmentation method specially designed for processing non-fat suppressed breast MRI [8]. This method is that the pectoral s muscle and breastair boundaries reveal smooth sheet-like surfaces in 3D, that can be simultaneously enhanced by a Hessian-based filter [9]. The strength of the Hessian-based filter correlates with the shape and different information of the structure. The method consists of four major steps: enhancing sheet-like structures, segmenting the pectoralis muscle boundary which defines the lower border of the breast region, segmenting the breast-air boundary which delimits the upper border of the breast portion, and filtering the region between the upper and lower borders which finally captures the area of breast tissue. More feature extraction techniques are proposed and examined in coincidence with lesion detection. The contrast media uptake rate and washout rates in benign and malignant breast lesions were verified using an empirical mathematical model (EMM), and model parameters are compared in lesions with mass-like and nonmass like enhancement properties [10]. The computer-aided interpretation of time-signal series as calculated during a DCE-MR analysis for each image voxel represents one of the main steps in designing CAD systems for breast MRI. In [2], it was shown that the shape of the time-signal intensity curve represents a main criterion in differentiating benign and malignant enhancing lesions in DCE-MR imaging. Morphological properties contain major information about a lesion s type. Combined with kinetic characteristics, one could expect a better accuracy. Furthermore, non-mass enhancing lesions such as DCIS can be well differentiated based on morphologic characteristics. The features that describe the geometric characteristics of the shape and local moments such as that of Krawtchouk to figure out the non-smooth surface [5]. Also kinetic features are extracted [11]. Discriminant analysis represents a major area of multivariate statistics and discovers a huge application in medical imaging problems. The well-known approaches are Bayes classification based linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)[5], Fisher s canonical discriminant analysis, random forest and support vector machines[5,8]. 3. Methods & Diagnostic Tasks. 3.1 Filtering: It is known that the noise in the MRI obeys a Rician distribution. The Rician noise is signal dependent and consequently differentiating signal from the noise is a tough task. Rician noise is also problematic in low signal-to-noise ratio (SNR) regimes where it not only affects random fluctuations, but also introduce a signal-dependent bias to the data which decreases image contrast. So here derive a Wavelet-domain filtering methods for Rician noise elimination. The new wavelet-domain filter decreases Rician noise contamination in both high and low SNR regimes. 3.2 Motion Correction: Automatic correction of image artifacts induced by involuntary motions and muscle relaxation during the image acquisition is essential for the computer-aided diagnosis of breast DCE-MRI. The extraction of actual time-intensity curves (TIC) for a specific region of interest (ROI) also relies on the motion compensation. Image registration techniques are employed for this task. To prevent lesion degeneration, the first post-contrast image is commonly chosen as the reference image and the pre-contrast and other subsequent temporal images are registered to the reference by minimizing the error with respect to one or more of the available similarity metrics between image pairs, adhering to the constraints imposed by the 567

4 underlying model. In this work, we use a scheme, which combines local elastic and global affine transformations into one deformation field [5]. Figure 1: The integrated framework comprising pre-processing and lesion analysis modules As illustrated in Figure 3.2, our method is able to eliminate the artificial parenchyma enhancement induced by patient motion. Figure 3.2: The subtraction image before (left) and after (right) the motion correction: the artificial parenchyma enhancement caused by motions (in red circle) is dramatically removed. 568

5 3.3 Breast Segmentation: To decrease false positive findings, a precise segmentation of the breast is the fundamental step to facilitate further diagnostic tasks. We previously implemented a fully automatic segmentation method specially designed for processing non-fat suppressed breast MRI [6]. The observation of this method is, the pectoralis muscle and breast-air boundaries shows smooth sheet-like surfaces in 3D,which can also be enhanced by a Hessianbased filter [7]. The enhancement strength of the designed Hessian-based filter correlates with the shape and the contrast information of the structures. The accuracy and robustness of this method are evaluated by extensive tests conducted with the data from different sites. Combined with the motion correction, breast masks obtained from pre-contrast images can be used to mask subsequent temporal or subtraction images which dramatically increases the accuracy in detecting suspicious regions, since it removes many resembling enhancements patterns elsewhere most notably in the heart. Figure 3.3 Results of breast segmentation:3d visualization of segmented pectoralis muscle and breast tissue (left);a representative slice of breast-air boundary (blue) and pectoral muscle boundary (purple) 3.4 Detection of Suspicious Regions: Lesion candidates are defined as abnormal contrast enhancement patterns found in DCE-MRI. The building up of contrast agent in areas with leaky vascularity is reflected by hyper-intense signals in T1-weighted DCE-MRI. Contrast agent accumulates in the region of a lesion which triggers fast (hence leaky) vascular growth to feed its excessive oxygen needs. Nonmass lesions often exhibit mild enhancement compared to invasive cancers, in fact even to be covered with normal parenchymal enhancement or benign lesions. However, a key clinical observation is that the improvement of nonmass lesions is often asymmetric: it appears unilateral. Oppose to nonmass lesions, parenchyma enhancement and some benign lesions enhance bilaterally. This typical enhancement characteristic previously led us to develop a detection algorithm of suspicious regions by investigating the asymmetry characteristics of left and right breasts [8].This method compares the symmetry properties in each 2D slice of a given 3D subtraction image.first, the probabilistic distribution of intensity transition patterns in one breast is learned. Then this probabilistic distribution is approximated by analyzing a 2D histogram.then,the learned probability map is applied to the contralateral breast. The rare transition patterns with extremely low probabilities are contemplated as suspicious. By applying a threshold with a value of almost close to zero (0.01 in this work) on probability map, obtain a binary mask of suspicious regions. This same 569

6 learning strategy is repeated inversely through all slices until all suspicious regions are covered. Figure 3.4 depicts the successive steps of this detection algorithm [8] Figure 3.4 Detection of lesion candidates. Left: maximum intensity projection (MIP) of the subtraction image, showing the extent of disease. Middle: the suspicion map shows dark areas of low probability events. Right: lesion candidates obtained by thresholding. 4. Feature Extraction 4.1 Kinetic Features: Kinetic features interpret the characteristics of time-intensity curves draw out from suspicious regions. Most clinical studies prove that the kinetic features are of great diagnostic values in discriminating malignant from benign lesions for mass lesions. There for, most kinetic features tend to be less descriptive in diagnosing nonmass lesions, because statistically the differences of kinetic features between malignant and benign nonmass lesions were not found [3].In this work, carefully choose a set of features that are documented to be most salient in diagnosing nonmass lesions. Here calculate the uptake rate, wash-out rate, maximum enhancement and time-to-peak from a dynamic sequence. The uptake rate and wash-out rate determine the change of contrast agent density by computing the slopes of intensity change over time in wash-in and wash-out phases. A higher wash-out rate shows a higher likelihood of malignancy, or of invasive lesions.moreover,the enhancement strength correlates with contrast agent concentration and thereby with the leakiness of the vasculature, and the time-topeak value indicates how leaky the vasculature is.for each suspicious lesion, here average the voxels whose feature values are sufficiently noticeable. 4.2 Morphological and Texture Features; The diagnostic accuracy of nonmass lesion CAD is anticipated to be improved by combining morphological and kinetic features [9].In this work, here incorporate a set of distribution descriptors including elongation factor, flatness, eccentricity, principle axis, eigenvalues, skewness, circularity and ellipticity. These features explain the intrinsic morphological properties of a given suspicious region from different responsive. Furthermore, we calculate the local binary patterns (LBP) of suspicious regions in the subtraction images [10]. 4.3 Feature Normalization: To normalize all the features and reduce the deviation between the features. It is nothing but need to normalize it by total mean and standard deviation. 4.4 Knowledge-based Analysis of Suspicious Regions: In this work, multi-class classifiers are trained to classify suspicious regions into three classes. Finally, given a new input data, the classifier, which achieves the best performance in training stage, will be directly used as an 570

7 automated diagnostic tool to classify suspicious regions. Support vector machines (SVMs) represent a major technique for lesion classification in medical imaging [8]. SVM is a new and promising classification method. It is a margin classifier that draws a hyper plane in the feature vector space; this defines a boundary that enlarges the margin between data samples in two classes, therefore leads to good generalization characteristics. Bayes classifier always provides optimal classification but requires exact knowledge of class prior probabilities and class conditional probabilities of features. The accurate classification results are obtained using SVM classifier which has high prediction and accuracy rate as compared to Bayes classifier. 4.5 Disease Area Segmentation: To find the tumor portion after finding the tumor image by segmentation method. Here using ostu thresholding for finding the area of the tumor.otsu s method selects the threshold by minimizing the within-class variance of the two groups of pixels separated by the thresholding operator. It does not depend on modeling the probability density functions; however, it assumes a bimodal distribution of gray-level values. 5. Conclusion In this paper, a fully automatic screening of nonmass lesions in breast DCE-MRI is presented. By analyzing the results in the filtering step that eliminate the Rician noise and the designed Hessianbased filter proven to be an accurate and robust tool for enhancing major sheet-like structures: the breast-air and the pectoralis muscle boundaries. In the proposed system uses SVM for the classification purpose. That accurately classified the lesions and also proven their robustness on the clinical data. 6. References [1] Lei Wang, A robust and extendable framework towards fully automated diagnosis of nonmass lesions in breast dce-mri,ieee International Symposium on Biomedical Imaging,2014. [2]S. A. Jansen., The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast., Journal of magnetic resonance imaging, vol. 33,no. 6, pp , [3] C. Kuhl, MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study, Lancet, vol. 370, no. 9586, pp , Aug [4] S. A. Jansen et al., DCEMRI of breast lesions: Is kinetic analysis equally effective for both mass and nonmass-like enhancement?, Medical Physics, vol. 35,no. 7, pp. 3102, [4] C. Plant et al., Computer-aided diagnosis of small lesions and non-masses in breast MRI, SPIE Medical Imaging, vol. 8367, pp A 83670A 9, [6] T. Boehler et al., A combined algorithm for breast MRI motion correction, SPIE Medical Imaging, vol. 6514, pp R 65141R 10, [7] L.Wang et al., Fully automatic breast segmentation in 3D breast MRI, IEEE International Symposium on Biomedical Imaging, pp , [8] L. Wang et al., Fully automated segmentation of the pectoralis muscle boundary in breast MR images, SPIE Medical Imaging, vol. 7963, pp , [9] A. Srikantha et al., Symmetry-based detection and diagnosis of DCIS in breast MRI, SPIE Medical Imaging, vol. 8670, pp E 86701E 8, [10] S. A. Jansen et al., The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast., Journal of magnetic resonance imaging, vol. 33,no. 6, pp , June [11] T. Ojala et al., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no. 7, pp , 2002 [12] A. Homeyer et al., A Generic Concept for Object-based Image Analysis., Proc.International Conference on Computer Vision Theory and Applications, vol. 2, pp ,

8 About Author s Ist. Anjali J. is currently pursuing M.Tech. in field of Computer Engineering from MCET. 2 nd. Prof. Reji George, currently the Head and Professor of the Computer Science & Engineering Department in MCET is a Post Graduate with MTech from MS University in Digital Image Processing with his 10 years of experience in teaching field. His areas of specialization include Digital Image Processing, Networking, Database and Programming. His computer skills include programming languages like C, JAVA & VB6. 572