Abstract
Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.In this paper, have tried to implement an automated segmentation method of gray level CT images. The segmentation process is performed by using the Fuzzy C-Means (FCM) clustering method to detect and segment kidney CT images for the kidney region. The propose method is started with pre-processing of the kidney CT image to separate the kidney from the abdomen CT and to enhance its contrast and removing the undesired noise in order to make the image suitable for further processing. The resulted segmented CT images, then used to extract the tumor region from kidney image defining the tumor volume (size) is not an easy task, because the 2D tumor shape in the CT slices are not regular. To overcome the problem of calculating the area of the convex shape of the hull of the tumor in each slice, we have used the Frustum model for the fragmented data.
Highlights
Kidneys cancer is one of the most common cancers worldwide, with increasing morbidity and high mortality [1]
Accurate kidneys segmentation from abdominal Computed Tomography (CT) scans is critical for computer-assisted diagnosis Computer-Aided Diagnosis (CAD) and therapy, including patient specific kidneys anatomy evaluation, functional assessment, treatment planning, and image-guided surgery [2]
An image can be represented in various feature spaces, and the Fuzzy C-Means (FCM) algorithm classifies the image by grouping similar data points in the feature space into clusters
Summary
Kidneys cancer is one of the most common cancers worldwide, with increasing morbidity and high mortality [1]. The Fuzzy C-Means (FC) method was implemented to detect and extract tumors and abnormalities in CT scan kidney images. An image can be represented in various feature spaces, and the FCM algorithm classifies the image by grouping similar data points in the feature space into clusters This clustering is achieved by iteratively minimizing a cost function that is dependent on the distance of the pixels to the cluster centers in the feature domain. The area of the extracted tumor region is computed by counting the number of pixels which have the value (1) in the image array. Where n [ ] represents the count of number of the patterns within the parenthesis [[[
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