Optimized Segmentation of Cellular Tomography through Organelles' Morphology and Image Features

Authors

  • Nur Intan Raihana Ruhaiyem Universiti Sains Malaysia, 11800 USM Penang, Malaysia.
  • Ahmad Sufril Azlan Mohamed Universiti Sains Malaysia, 11800 USM Penang, Malaysia.
  • Bahari Belaton Universiti Sains Malaysia, 11800 USM Penang, Malaysia.

Abstract

Computational tracing of cellular images generally requires painstaking job in optimizing parameter(s). By incorporating prior knowledge about the organelle’s morphology and image features, the required number of parameter tweaking can be reduced substantially. In practical applications, however, the general organelles’ features are often known in advance, yet the actual organelles’ morphology is not elaborated. Two primary contributions of this paper are firstly the classification of insulin granules based on its image features and morphology for accurate segmentation – mainly focused at pre-processing image segmentation and secondly the new hybrid meshing quantification is presented. The method proposed in this study is validated on a set of manually defined ground truths. The study of insulin granules in particular; the location, and its image features has also opened up other options for future studies.

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Published

2016-06-01

How to Cite

Ruhaiyem, N. I. R., Mohamed, A. S. A., & Belaton, B. (2016). Optimized Segmentation of Cellular Tomography through Organelles’ Morphology and Image Features. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(3), 79–83. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1006

Issue

Vol. 8 No. 3: Emerging Technologies in Communication and Computer Engineering for Smart City Applications I

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Articles

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