A Gaussian process and derivative spectral-based algorithm for red blood cell segmentation

Yingying Xue; Jianbiao Wang; Mei Zhou; Xiyue Hou; Qingli Li; Hongying Liu; Yiting Wang

doi:10.1117/12.2281990

21 July 2017 A Gaussian process and derivative spectral-based algorithm for red blood cell segmentation

Yingying Xue, Jianbiao Wang, Mei Zhou, Xiyue Hou, Qingli Li, Hongying Liu, Yiting Wang

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202C (2017) https://doi.org/10.1117/12.2281990
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

As an imaging technology used in remote sensing, hyperspectral imaging can provide more information than traditional optical imaging of blood cells. In this paper, an AOTF based microscopic hyperspectral imaging system is used to capture hyperspectral images of blood cells. In order to achieve the segmentation of red blood cells, Gaussian process using squared exponential kernel function is applied first after the data preprocessing to make the preliminary segmentation. The derivative spectrum with spectral angle mapping algorithm is then applied to the original image to segment the boundary of cells, and using the boundary to cut out cells obtained from the Gaussian process to separated adjacent cells. Then the morphological processing method including closing, erosion and dilation is applied so as to keep adjacent cells apart, and by applying median filtering to remove noise points and filling holes inside the cell, the final segmentation result can be obtained. The experimental results show that this method appears better segmentation effect on human red blood cells.

Citation Download Citation

Yingying Xue, Jianbiao Wang, Mei Zhou, Xiyue Hou, Qingli Li, Hongying Liu, and Yiting Wang "A Gaussian process and derivative spectral-based algorithm for red blood cell segmentation", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202C (21 July 2017); https://doi.org/10.1117/12.2281990

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