Prostate Segmentation and Tumor Detection from MR Images Using Latent Features

Kharote, Prashant Ramesh; Sankhe, Manoj; Patkar, Deepak

doi:10.1109/indicon47234.2019.9030316

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…An average DSC score of 92% for a complete brain tumor, 81% for brain tumor core, and 83% for an enhanced brain tumor was recorded which yielded more than the conventional approach adopted. The study of Kharote, Sankhe, & Patkar (2019) also segmented the prostate from Multiparametric Magnetic Resonance Imaging as segmentation of the prostate is a complicated process due to the extensive variations of unclear prostate boundaries in prostate shape. The database used T2-weighted prostate MR images of 184 subjects.…”

Section: Related Workmentioning

confidence: 99%

“…Extensive investigations has been made in recent years with their integration to Computer-Aided Detection (CAD) systems for tumor segmentation making it an interesting tool to further investigate their performance across selected tumour cells in this study. Since the manual delineation of tumors is an arduous process with variations in results; it is therefore critical to develop an automated model for segmentation (Kharote, Sankhe, & Patkar, 2019;Chahal, Pandey, & Goel, 2020)).…”

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confidence: 99%

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Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells

Appati

Badzi

Soli

et al. 2021

International Journal of E-Health and Medical Communications

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This study aims to analyze the Chan-Vese model's performance using a variety of tumor images. The processes involve the tumors' segmentation, detecting the tumors, identifying the segmented tumor region, and extracting the features before classification occurs. In the findings, the Chan-Vese model performed well with brain and breast tumor segmentation. The model on the skin performed poorly. The brain recorded DSC 0.6949903, Jaccard 0.532558; the time elapsed 7.389940 with an iteration of 100. The breast recorded a DSC of 0.554107, Jaccard 0.383228; the time elapsed 9.577161 with an iteration of 100. According to this study, a higher DSC does not signify a well-segmented image, as the breast had a lower DSC than the skin. The skin recorded a DSC of 0.620420, Jaccard 0.449717; the time elapsed 17.566681 with an iteration of 200.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%