Lung Cancer Diagnosis from CT Images Using Fuzzy Inference System T.Manikandan 1, Dr. N. Bharathi 2 1 Associate Professor, Rajalakshmi Engineering College, Chennai-602 105 2 Professor, Velammal Engineering College, Chennai-600 066 1 mani_stuff@yahoo.co.in, 2 rathiraj_2000@yahoo.com Abstract. The Fuzzy Inference System (FIS) plays a vital role in the medical field to provide medical assistance to the radiologist to diagnose the abnormality in the medical images. This paper presents a scheme to improve the efficiency of the lung cancer diagnosis system by proposing the segmentation of the suspected lung nodules by region based segmentation and cancer identification by FIS. The proposed method is implemented in two phases. The first phase carries pre-processing for primary noise removal by wiener filter followed by region growing to segment the suspected lung nodules from CT lung images. The second phase carries the classification of the segmented nodules as either benign (normal) or malignant (cancerous) by extracting the features like diameter, shape and intensity values and given as the input to the FIS. The Fuzzy system finds the severity of the suspected lung nodules based on IF-THEN rules. The sensitivity of the proposed system is 92.3%, which show that the proposed work can help the radiologists to increase their diagnostic confidence. Keywords: Computed Tomography (CT), Segmentation, Region growing, and Fuzzy Inference System (FIS). 1 Introduction Lungs are the essential organs for respiration (inspiration and expiration) situated at thoracic cavity. Today, the lung cancer is serious disease in the world causing large number of deaths [1]. The cells of all living organisms normally divide and grow in a control manner. When this control process is lost and tissues start expands then the situation is called cancer. Among the various cancers like bone cancer, breast cancer, blood cancer etc., the lung cancer is the most deadly one. The causes of lung cancer are cigarette smoking, inhalation of tobacco, lung diseases, air pollution etc. The research shows that smoking of cigarette is the leading cause of lung cancer (90%) in the world. The people they don t smoke (non-smokers) also get s lung cancer, however the chance of risk is 10 times lesser than the smokers. The most preferred option for treating the lung cancer in the final stage is surgical removal of the diseased lung. Hence it is necessary to detect the lung cancer at an early stage to limit the danger. In this paper, lung cancer diagnosis system based on Fuzzy logic is proposed to detect the lung cancer at the early stage. The proposed system first segments the V.V. Das and N. Thankachan (Eds.): CIIT 2011, CCIS 250 pp. 474 479, 2011. Springer-Verlag Berlin Heidelberg 2011
T Manikandan and N Bharathi 475 suspected lung nodules from the input lung image and classifies either benign or cancerous based on the feature extraction. Then the extracted features are given to the input of FIS. The Fuzzy Inference System makes the decision based on IF-THEN rules. This paper is organized as follows: Section 2 describes the related works, Section 3 deals the proposed work. Section 4 gives the experimental results and finally section 5 draws the conclusion and Future work. 2 Related Work 2.1 Artificial Neural Networks The Artificial Neural Networks (ANN) refers to computing systems whose central theme is borrowed from the analogy of biological neural networks. Neural networks can be configured in various arrangements to perform range of tasks such as machine vision, pattern recognition, data mining, text mining, classification and optimization. The most commonly used Neural Networks (NN) are feed forward and back propagation NN. The ANN consists of three layers namely input layer, hidden layer and output layer. The ANN is trained with the set of feature vectors. The training may be of any one of the following: supervised learning, unsupervised learning and reinforcement learning. In the literature there are number of proposals for diagnosing the cancerous nodules from lung images. To assess the benefit of ANN for diagnosing lung cancer the systematic review was conducted by N.Ganesan et. al [2]. The CAD system to diagnose the lung cancer based on ANN to assist radiologists in distinguishing malignant from pulmonary nodules was proposed by Yongjun et. al [3]. In neural network based classifier, the difficult task is designing of ANN structure. To provide the solution for the above problem Genetic algorithm (GA)- ANN hybrid intelligence was described by Fazil Ahmad et. al [4]. In their approach GA was used to select significant features simultaneously as input to ANN and optimal number of hidden node is determined automatically. Every intelligent technique has particular computational properties that make them suited for particular problems and not for others. For example, the NN are good at recognizing patterns, they need more processing time and lot of past data to make the decision. 2.2 Fuzzy Logic Fuzzy logic is a rule based system it uses if-then rules. Fuzzy logic has been found applications in many areas from control theory to artificial intelligence. An automated method to detect the lung nodules based on wavelet transform, bi-histogram equalization, morphology filter and Fuzzy logic was proposed by C.Clifford Samuel et. al [5], however in their work the bronchovascular details are also detected as nodules. M.A.Saleem Durai et. al [6] described the technique to determine disease name, stage and diagnostic treatment of the cancer patient using Fuzzy rules but the diagnosis were complex because of the analysis of all the information gathered about the symptoms. To overcome the drawbacks of ANN and existing Fuzzy systems the
476 Lung Cancer Diagnosis from CT Images new system is proposed to diagnose the lung cancer. 3 Proposed Work The proposed work is carried in two phases. In first phase the suspected lung nodule are segmented. In the second phase the Fuzzy Inference System makes the diagnosis from the extracted features from the suspected nodules. The flow diagram of the proposed method is shown in Fig. 1. The proposed method consists of three steps. They are pre-processing, suspected lung nodule segmentation and feature extraction and Fuzzy Inference System. Fig. 1 Flow diagram of the proposed method 3.1 Pre -processing The pre-processing aims to reduce the noise in the input CT lung images. The preprocessing is achieved by suppressing the noise. The noise distribution in CT images follows the Gaussian distribution therefore Wiener filter is employed to remove the noise. The Wiener filter is an optimum filter which minimizes the means square error hence it maximizes the image quality. In order to balance between the noise removal and over blurring the size of the Wiener filter is selected as 3x3. The metrics used to evaluate the performance of filters are Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The PSNR is a measure of the peak error whereas the MSE is the cumulative squared error between the filtered and the original image. The mathematical formulae for the above two are, MSE = 1/MN{ [g(x, y) - g (x, y) ] 2 } (1) PSNR=20*log 10 [255/sqrt(MSE)] (2) Where, g(x, y) is the original image, g (x, y) is the filtered image and M, N are the dimensions of image. 3.2 Suspected Lung Nodule Segmentation and Feature Extraction The next step after pre-processing is the segmentation of the suspected lung nodules from the CT images. The suspected lung nodules are segmented by region based segmentation. In segmentation technique, the complex image is divided into small sub images. The segmentation should be stopped when the Region of Interest (ROI) of a particular application is isolated. The ROI in CT image is usefully a cluster of pixels that appears to be dense mass. The region based segmentation uses region growing
T Manikandan and N Bharathi 477 technique, which is based on the similarity approach to segment the suspected lung nodules. The region growing starts with set of seed points. The region is allowed to grow by appending the neighbouring pixels that have properties similar to the seed such as specific gray level. Thus the region based segmentation segments the suspected lung nodules from the input CT lung images. The selectivity of the cancer detection depends on the ability to segment the suspected nodules from the lung images. The various features like diameter, shape and intensity are extracted for the suspected lung nodules. These features are the input to the Fuzzy Inference System. 3.3 Fuzzy Inference System Fuzzy inference is the process of formulating the mapping from a given input to an output using Fuzzy logic. The mapping then provides a basis from which decisions can be made. The overall name for a system is, that uses fuzzy reasoning to map an input space into an output space. Fuzzy logic is a rule based system it uses if-then rules. The Fuzzy logic is proved to be a potential tool for decision making systems such as pattern recognition and expert systems. FIS is used to recognize the nodules depending on the input, output Fuzzy membership functions. Extracted features like diameter, shape and intensity are the input to the Fuzzy system. Based on the extracted features the FIS can classify whether the suspected nodules are normal or cancerous one. The cancerous nodules have greater diameter and intensity than the normal lung nodules. Further the cancerous nodules will have irregular shape. The severity of the detected nodule is computed using Fuzzy rules framed relating to the extracted features. The rules used in FIS to diagnose the lung cancer (based on the observation of the radiologists and field experts) is as follows, IF (Diameter>10pixels, Intensity>240) and IF (Shape is irregular?) THEN Disease = Cancer Else Normal cells are found. 4 Experimental Results The lung cancer diagnosis using FIS is shown in Fig. 2.The input lung image acquired from CT image [see Fig. 2(a)] is first pre-processed by Wiener filter to remove the noise. After the removal of noise the PSNR value is calculated as 40.6 decibels. Hence the Wiener filter maximizes the quality of the input image and reduces the means square error of the counterpart. The filtered image is given in Fig. 2(b). After pre-processing, the region growing technique is applied on the input lung image to segment the suspected lung nodules. The region growing is shown in Fig. 2(c).
478 Lung Cancer Diagnosis from CT Images Fig. 2 Lung cancer diagnosis using FIS (a) Input CT image (b) Filtered image (c) Region growing (d) Suspected lung nodules (e) 3D view of malignant nodules (f) Dialog box displaying the result The segmented suspected lung nodules are shown in Fig. 2(d), which show that they are cluster of pixels that appears to be dense mass. The suspected lung nodules may be benign or malignant. The various features are extracted for the suspected nodules and given as input to the FIS. Based on the extracted features, the Fuzzy Inference System classifies whether the suspected nodules are benign or malignant. The 3D views of malignant nodules are given in Fig. 2(e). The dialog box displays the output of FIS is given in Fig. 2(f). The obtained results are compared with the radiologists for sensitivity, which is computed using the equation, Sensitivity = TP / (TP + FN) (3) Where TP is the True Positive nodules and FN is False Negative nodules. The test is carried out for 50 patient s data sets, out of which 25 patients data sets are taken with normal condition and 25 patient s data sets are taken with cancerous condition. The output of the FIS for the sample data sets are given in table 1. The sensitivity of the FIS is compared with the various existing methods, which is shown in table 2. The obtained results show that the sensitivity of the proposed method is higher than the other methods. Table 1. Output of FIS for sample data Table 2. Quantitative comparison of FIS with other methods
T Manikandan and N Bharathi 479 5 Conclusion and Future work In this paper, the lung cancer diagnosis system based on FIS is proposed. The proposed system consists of pre-processing, segmentation of the suspected nodules and classification to detect the lung cancer. The sensitivity of the system is calculated as 92.3%, which is higher than the existing methods. Therefore the lung cancer diagnosis system based on FIS can be useful for the radiologists to diagnose the lung cancer at the early stage. However in future, the sensitivity of the proposed system may improve by using the hybrid systems i.e., combining either Neural Network with Fuzzy logic (Neuro-Fuzzy network) or Fuzzy logic with Neural Network (Fuzzy- Neural) network. References 1. Asbestos cancer resource, http://www.asbestos.net/lung-cancer/lung-cancer-facts 2. Ganesan, N., Venkatesh, K., Rama, M.A.: Application of Neural Networks in diagnosing cancer diseases using Demographic data. In: International journal of computer applications, vol.1, pp. 76-82 (2010) 3. Yongjun WU, Na Wang, Hongshezhang.: Application of Artificial Neural Networks in the Diagnosis of Lung Cancer by Computed Tomography. In: Sixth International Conference on Natural Computation, pp. 147-153, China (2010) 4. Fadzil Ahmad, Nor Ashidi Mat-Isa.: Genetic algorithm-artificial Neural network Hybird intelligence for cancer diagnosis. In: Second international conference on Computational Intelligence, Communication Systems and Networks (2010) 5. Clifford Samuel, C., Saravanan, V., Vimala Devi, M.R.: Lung nodule diagnosis from CT images using fuzzy logic. In: International Conference on Computational Intelligence and Multimedia Applications (2007) 6. Saleem Durai, M.A., Iyengar, N.Ch.S.N.: Effective analysis and diagnosis of lung cancer using Fuzzy rules. In: International journal of Engineering Science and Technology, Vol 2(6), pp. 2102-2108 (2010) 7. Kenji Suzuki, Junji Shairaishi.: False-Positive reduction in computer-aided diagnostic scheme for detecting in chest radiographs by means of massive training artificial neural network, IEEE transaction on medical imaging, vol. 24, pp.1138-1143 ( 2005) 8. Ayman El-Baz, Robert Falk.: Promising results for early diagnosis of lung cancer. In: IEEE transactions on Medical imaging, pp. 1151-1154 (2008)