Journal of Innovative Image Processing
IRO Journals
Volume 1 — Issue 2 — December 2019

BIOMEDICAL IMAGE ANALYSIS USING SEMANTIC SEGMENTATION

https://doi.org/10.36548/jiip.2019.2.004
Bindhu V
Professor and Head, Department of Electronics and Communication Engineering, PPG Institute of Technology, Villankurichi Saravanampatti P.O, Coimbatore, India
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V, Bindhu. 2019. “BIOMEDICAL IMAGE ANALYSIS USING SEMANTIC SEGMENTATION”. Journal of Innovative Image Processing 1 (2): 91-101. https://doi.org/10.36548/jiip.2019.2.004.
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Keywords

— Fully Connected Convolutional Networks
— Biomedical Images
— Down Sampling
— Up Sampling
— Encoder/Decoder
Published: 31-12-2019

Abstract

Semantic Segmentation is a very active area of research in the examining the medical images. The failure in the conventional segmentation methods to preserve the full resolution throughout the network led to the research's that developed methods to protect the resolution of the images. The proposed method involves the semantic segmentation model for the biomedical images by utilizing the encoder/decoder structure to down sample the spatial resolution of the input data and develop a lower resolution feature mapping that are very effective at distinguishing between the classes and then perform the up samples to have a full-resolution segmentation map of the biomedical images reducing the diagnostic time. The frame work put forth utilizes a pixel to pixel fully trained cascaded convolutional neural network for the task of image segmentation. The evaluation biomedical image analysis using the semantic segmentation shows the performance improvement achieved by the minimization of the time required in testing and the augmentation in the analysis performed by the radiologist.

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