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Related Experiment Video

Updated: Mar 8, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with

Saif Dawood Salman Al-Shaikhli¹, Michael Ying Yang², Bodo Rosenhahn³

¹School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA; Institut für Informationsverarbeitung, Leibniz Universität Hannover, Appelstr. 9A, 30167 Hannover, Germany.

Computer Methods and Programs in Biomedicine

|January 24, 2017

Summary

This study introduces a new method for Alzheimer's disease classification using 3D caudate nucleus segmentation. Focusing on caudate nucleus atrophy improves detection accuracy compared to whole brain analysis.

Keywords:

3D segmentation Alzheimer Caudate nucleus Dictionary learning MRI-T1 medical image

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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Published on: June 9, 2018

Area of Science:

Medical Imaging
Neuroscience
Artificial Intelligence

Background:

Alzheimer's disease diagnosis relies on identifying brain structural changes.
Current methods often analyze whole brain structures, potentially missing subtle regional atrophy.

Purpose of the Study:

To develop an automated 3D caudate nucleus segmentation method for Alzheimer's disease classification.
To evaluate the efficacy of caudate nucleus atrophy analysis in Alzheimer's detection.

Main Methods:

A novel level set cost function incorporating sparse representation via coupled dictionary learning (grayscale image features and caudate nucleus labels).
Online dictionary learning to adapt dictionaries from training data.
Region-based feature dictionary learned from caudate nucleus shape features for classification.

Main Results:

Achieved high accuracy in both segmentation (91.5%) and classification (92.5%).
Demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

Caudate nucleus atrophy is a significant indicator for Alzheimer's disease detection.
This automated segmentation and classification approach offers an advantage over whole brain analysis.