Robust Medical Image Segmentation for Accurate Computer Aided Diagnosis

Abstract

Image processing is being successfully applied in many areas medical research such as computer aided diagnosis, tumor imaging and treatment, angiography, and carotid artery plaque detection. For medical image analysis, segmentation is an intermediate step to segregate region of interest from the background. The ultimate goal of segmentation is to identify the part of the data array that makes up an object in the real world. Many imaging modalities are in practice for disease diagnosis. Among those, owing to noninvasive nature, ultrasound imaging provides an invaluable tool for disease diagnosis. Major limitations faced by ultrasound imaging modality include low quality, inherent noise, and wave interferences. Consequently, a substantial effort from radiologists is required to extract constructive information about a particular disease. In this regard, an efficient and accurate computer aided diagnostic system for ultrasound images is highly desirable for disease (plaque) diagnosis. Carotid arteries are vital arteries that supply oxygen rich blood to the brain. Carotid artery stenosis is the process of narrowing the carotid artery due to the presence of atherosclerosis. The plaque may partially or fully block the blood flow to the brain and the probability of cerebrovascular stroke becomes high. Ultrasound imaging is used for detection of plaque in carotid artery. Due to lower quality and other degradations, segmentation of carotid arteries ultrasound images becomes a challenging task. In this thesis, several segmentation techniques are proposed, which successfully segment the carotid artery ultrasound images. Firstly, we have proposed spatial fuzzy c-means modified (sFCMM) clustering technique and also investigated effectiveness of ensemble clustering. The proposed sFCMM technique assigns weight to each pixel in a sub-window according to the pixel’s contribution. The proposed scheme required image pre-processing for noise reduction and hence segmentation has been performed on filtered image. In another approach, we propose information gain based fuzzy c-means clustering (IGFCM) algorithm that avoids the preprocessing step and still yields better results compared to sFCMM technique. The IGFCM approach exploits the concept of information gain to automatically update the xvii fuzzy membership function and cluster centeriods. However, from IGFCM segmented images, it has been observed that some of the pixels of arterial walls are mislabeled by IGFCM. In order to overcome this problem, a semi-supervised clustering approach named robust segmentation and classification of ultrasound images (RSC-US) has been proposed to segment carotid artery ultrasound images. The RSC-US approach is composed of three phases. In the first phase, the fuzzy inference system (FIS) is generated. In second phase, carotid artery ultrasound images are segmented based on the generated FIS. Finally, a decision making system has been designed to segregate the segmented images into normal or abnormal subjects. The RSC-US approach did not utilize the spatial information of pixel’s which plays a vital role in segmentation. Consequently, the spatial information has also been explored and a new approach named robust fuzzy radial basis function networks (RFRBFN) has been proposed to segment carotid artery ultrasound images. The RFRBFN segments the carotid artery ultrasound images with high precision. Due to the Lagrange function and a smoothing parameter, the RFRBFN might be computationally expensive. Finally, an automatic active contour based segmentation technique for carotid artery ultrasound images is proposed. This technique can successfully segment natural scene as well as medical images.