Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model. Issue 47 (20th November 2020)

• Record Type: Journal Article
• Authors: Hsu, Wei-Yen; Lu, Chih-Cheng; Hsu, Yuan-Yu
• Other Names: Mao. Chao section editor.
• Is Part Of: Medicine. Volume 99:Issue 47(2020)
• Publication Date: 2020-11-20
• Subjects: active contour model -- image segmentation -- kidney cancers; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000023083
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of our study was to develop an adaptive ACM method to measure the radiological size of kidney cancer on computed tomography in the hospital patients. This paper proposed a set of medical image processing, applying images provided by the hospital and select the more obvious cases by the doctors, after the first treatment to remove noise image, and the kidney cancer contour would be circled by using the proposed adaptive ACM method. The results showed that the experimental outcome has highly similarity with the medical professional manual contour. The accuracy rate is higher than 99%. We have developed a novel adaptive ACM approach that well combines a knowledge-based system to contour the kidney cancer size in computed tomography imaging to support the clinical decision.