Computer aided solution for segmenting the neuron line in hippocampal microscope images

Tahseen Albaidhani; Sabah Jassim; Hisham Al-Assam

doi:10.1117/12.2268473

10 May 2017 Computer aided solution for segmenting the neuron line in hippocampal microscope images

Tahseen Albaidhani, Sabah Jassim, Hisham Al-Assam

Proceedings Volume 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017; 1022109 (2017) https://doi.org/10.1117/12.2268473
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

The brain Hippocampus component is known to be responsible for memory and spatial navigation. Its functionality depends on the status of different blood vessels within the Hippocampus and is severely impaired by Alzheimer's disease as a result blockage of increasing number of blood vessels by accumulation of amyloid-beta (Aβ) protein. Accurate counting of blood vessels within the Hippocampus of mice brain, from microscopic images, is an active research area for the understanding of Alzheimer’s disease. Here, we report our work on automatic detection of the Region of Interest, i.e. the region in which blood vessels are located. This area typically falls between the hippocampus edge and the line of neurons within the Hippocampus. This paper proposes a new method to detect and exclude the neuron line to improve the accuracy of blood vessel counting because some neurons on it might lead to false positive cases as they look like blood vessels. Our proposed solution is based on using trainable segmentation approach with morphological operations, taking into account variation in colour, intensity values, and image texture. Experiments on a sufficient number of microscopy images of mouse brain demonstrate the effectiveness of the developed solution in preparation for blood vessels counting.

Citation Download Citation

Tahseen Albaidhani, Sabah Jassim, and Hisham Al-Assam "Computer aided solution for segmenting the neuron line in hippocampal microscope images", Proc. SPIE 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017, 1022109 (10 May 2017); https://doi.org/10.1117/12.2268473

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