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Robust Energy-Based Image Segmentation Using Nonparametric Joint Shape and Feature Priors

Year: 2026

Type: article

Abstract: Image segmentation is a key task in computer vision that identifies important structures in images. Traditional methods like thresholding and region-based segmentation often struggle with noise and changes in shape. This paper introduces a segmentation approach that uses nonparametric joint shape and feature priors to enhance accuracy. Shape priors are learned from the MNIST dataset, and feature representations are extracted through Principal Component Analysis (PCA). We formulate the segmentation as an energy minimization problem that combines data fidelity, shape, and feature terms. Experimental results, measured using Dice coefficient and Hausdorff distance, show the effectiveness of this method.

Source: Iconic Research and Engineering Journals

DOI: 10.64388/irev9i9-1715105

Authors: Dr. B. Hemanth Kumar, Mopuri Srinivas, Shaik Asadhulla, Pammi Pranay, Panguluri Rajesh

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W7137874370

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