A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images

Glomerular basement membrane segmentation is an ultimate step in several image processing applications for kidney diseases and abnormalities in microscopic images. However, extracting the glomerular basement membrane (GBM) regions accurately is considered challenging because of the large variants in the microscopic images. The contribution of this work is to propose a computer-aided detection system to provide accurate GBM segmentation.

A novel GBM segmentation algorithm is developed based on neutrsophic set and shearlet transform. Firstly, the shearlet features are extracted from the microscopic image samples using shearlet transform. Afterward, the neutrosophic image is defined using shearlet features, and the indeterminacy on the neutrosophic image is reduced using an α-mean operation. Lastly, the k-means clustering algorithm is applied to segment the neutrsophic image and the GBM is identified using its intensity feature.

Three metrics, namely the average distance (AvgDist), the Hausdorff distance (Hdist), and percentage overlap area (POA); were employed to assess the proposed method performance. The results established that the proposed method achieved smaller distance errors and larger POAs. For the tested image, the average of AvgDist, HDist and POA are 1.99204, 4.59535 and 0.67857, respectively. The results established that the cases were segmented accurately using the proposed NS based shearlet transform.

The new method utilizing the shearlet features and neutrosophic set improved the accuracy of GBM segmentation. Further study is underway to improve an automated CAD system using the refined segmentation results.