Brain ventricle is one of the biomarkers for detecting neurological disorders. Studying the shape of the ventricles will aid in the diagnosis process of atrophy and other CSF-related neurological disorders, as ventricles are filled with CSF. This paper introduces a spectral analysis algorithm based on wave kernel signature. This shape signature was used for studying the shape of segmented ventricles from the brain images. Based on the shape signature, the study groups were classified as normal subjects and atrophy subjects. The proposed algorithm is simple, effective, automated, and less time consuming. The proposed method performed better than the other methods heat kernel signature, scale invariant heat kernel signature, wave kernel signature, and spectral graph wavelet signature, which were used for validation purpose, by producing 94–95% classification accuracy by classifying normal and atrophy subjects correctly for CT, MR, and OASIS datasets.

Graphical abstract

中文翻译：

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使用改进的 WKS 进行心室形状分析进行萎缩检测

脑室是检测神经系统疾病的生物标志物之一。研究脑室的形状将有助于萎缩和其他脑脊液相关神经系统疾病的诊断过程，因为脑室充满了脑脊液。本文介绍了一种基于波核特征的频谱分析算法。该形状特征用于研究来自大脑图像的分割脑室的形状。根据形状特征，将研究组分为正常受试者和萎缩受试者。所提出的算法简单、有效、自动化且耗时少。所提出的方法比用于验证目的的其他方法热核签名、尺度不变热核签名、波核签名和谱图小波签名表现更好，

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