Abstract
Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting the brain tumors in computed tomography images using Support Vector Machine classifier. The objective of this work is to compare the dominant grey level run length feature extraction method with wavelet based texture feature extraction method and SGLDM method. A dominant gray level run length texture feature set is derived from the region of interest (ROI) of the image to be selected. The optimal texture features are selected using Genetic Algorithm. The selected optimal run length texture features are fed to the Support Vector Machine classifier (SVM) to classify and segment the tumor from brain CT images. The method is applied on real data of CT images of 120 images with normal and abnormal tumor images. The results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of classification accuracy. From the analysis and performance measures like classification accuracy, it is inferred that the brain tumor classification and segmentation is best done using SVM with dominant run length feature extraction method than SVM with wavelet based texture feature extraction method and SVM with SGLDM method. In this work,we have attempted to improve the computing efficiency as it selects the most suitable feature extration method that can used for classification and segmentation of brain tumor in CT images efficiently and accurately. An avearage accuracy rate of above 97% was obtained usinh this classification and segmentation algorithm.
Highlights
IntroductionMedical CT Images have been applied in clinical diagnosis widely. That can assist physicians to detect and locate Pathological changes with more accuracy
In recent years, medical CT Images have been applied in clinical diagnosis widely
We discovered three methods which are i) Dominant gray level run length feature extraction method ii) Wavelet based feature extraction method iii) Spatial Gray Level Dependence Matrix method (SGLDM) method
Summary
Medical CT Images have been applied in clinical diagnosis widely. That can assist physicians to detect and locate Pathological changes with more accuracy. The images, if processed appropriately can offer a wealth of information which is significant to assist doctors in medical diagnosis. A lot of research efforts have been directed towards the field of medical image analysis with the aim to assist in diagnosis and clinical studies [1]. Pathologies are clearly identified using automated CAD system [2] It helps the radiologist in analyzing the digital images to bring out the possible outcomes of the diseases. The first reason is that scanner images contain anatomical information which offers the possibility to plan the direction and the entry points of radio therapy rays which have to target only the tumor region and to avoid other organs. The second reason is that CT scan images are obtained using rays, which is same principle as radio therapy. This is very important because the intensity of radio therapy rays have been computed from the scanned image
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