Automatic symmetry based cluster approach for anomalous brain identification in PET scan image : An Analysis

TL;DR

This paper explores symmetry-based clustering methods for segmenting PET brain images to improve early diagnosis of neurological and systemic diseases, aiding medical decision-making.

Contribution

It analyzes symmetry-based distances in clustering algorithms specifically for PET brain image segmentation, a novel application in medical imaging.

Findings

Symmetry-based clustering improves brain PET image segmentation accuracy.

The approach aids early detection of brain and systemic diseases.

Analysis of symmetry distances enhances understanding of PET image structures.

Abstract

Medical image segmentation is referred to the segmentation of known anatomic structures from different medical images. Normally, the medical data researches are more complicated and an exclusive structures. This computer aided diagnosis is used for assisting doctors in evaluating medical imagery or in recognizing abnormal findings in a medical image. To integrate the specialized knowledge for medical data processing is helpful to form a real useful healthcare decision making system. This paper studies the different symmetry based distances applied in clustering algorithms and analyzes symmetry approach for Positron Emission Tomography (PET) scan image segmentation. Unlike CT and MRI, the PET scan identifies the structure of blood flow to and from organs. PET scan also helps in early diagnosis of cancer and heart, brain and gastro intestinal ailments and to detect the progress of treatment. In this paper, the scope diagnostic task expands for PET image in various brain functions.