Shape Aware Automatic Region-of-Interest Subdivisions

TL;DR

This paper introduces a shape-aware method for automatically subdividing regions of interest in images, improving accuracy and efficiency over manual or grid-based approaches, with applications in medical image analysis of myocardial regions.

Contribution

The paper presents a novel shape-based subdivision technique that automatically generates meaningful subregions without relying on intensity or other variable criteria.

Findings

Effective in medical image analysis of myocardial regions

Produces subdivisions that better reflect the shape of regions

Reduces manual effort and subjectivity in region subdivision

Abstract

In a wide variety of fields, analysis of images involves defining a region and measuring its inherent properties. Such measurements include a region's surface area, curvature, volume, average gray and/or color scale, and so on. Furthermore, the subsequent subdivision of these regions is sometimes performed. These subdivisions are then used to measure local information, at even finer scales. However, simple griding or manual editing methods are typically used to subdivide a region into smaller units. The resulting subdivisions can therefore either not relate well to the actual shape or property of the region being studied (i.e., gridding methods), or be time consuming and based on user subjectivity (i.e., manual methods). The method discussed in this work extracts subdivisional units based on a region's general shape information. We present the results of applying our method to the medical image analysis of nested regions-of-interest of myocardial wall, where the subdivisions are used to study temporal and/or spatial heterogeneity of myocardial perfusion. This method is of particular interest for creating subdivision regions-of-interest (SROIs) when no variable intensity or other criteria within a region need be used to separate a particular region into subunits.