Statistical recursive minimum cross entropy for ultrasound image segmentation

Bedi, Anterpreet Kaur; Sunkaria, Ramesh Kumar

doi:10.1007/s11042-022-12050-8

Cited by 2 publications

(1 citation statement)

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“…In this paper, pixel classification networks to segment the kidneys automatically. A technique called recursive minimum cross entropy is used to perform efficient segmentation in [17]. The proposed method makes use of local statistics of the image.…”

Section: Related Workmentioning

confidence: 99%

Entropy Based Segmentation Model for Kidney Stone and Cyst on Ultrasound Image

George¹,

Bhimasena²

2022

IJC

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Segmentation of abnormal masses in kidney images is a tough task. One of the main challenges is the presence of speckle noise, which will restrain the valuable information for the medical practitioners. Hence, the detection and segmentation of the affected regions vary in accuracies. The proposed model includes pre-processing and segmentation of the diseased region. The pre-processing consists of Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) to improve the clarity of the images. Further, segmentation has been done based on the entropy of the image and gamma correction has been done to improve the overall brightness of the images. An optimal global threshold value is selected to extract the region of interest and measures the area. The model is analyzed with statistical parameters like Jaccard index and Dice coefficient and compared with the ground truth images. To check the accuracy of the segmentation, relative error is calculated. This framework can be used by radiologists in diagnosing kidney patients

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Section: Related Workmentioning

confidence: 99%