A Hybrid Semantic Model for MRI Kidney Object Segmentation with Stochastic Features and Edge Detection Techniques

A Hybrid Semantic Model for MRI Kidney Object Segmentation with Stochastic Features and Edge Detection Techniques
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© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Sitanaboina S L Parvathi, Harikiran Jonnadula
DOI : 10.14445/22315381/IJETT-V70I9P242

How to Cite?

Sitanaboina S L Parvathi, Harikiran Jonnadula, "A Hybrid Semantic Model for MRI Kidney Object Segmentation with Stochastic Features and Edge Detection Techniques" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 411-420, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P242

Abstract
Because of its importance in disease diagnosis, medical image analysis has been a prominent researchtopic for over a decade. Advancements in deep learning methodologies made computer-aided disease diagnosis feasible from medical images. Object semantic segmentation is the primary activity in medical image analysis. Various deterministic deep learning models for semantic segmentation of objects (organs) from medicalimages are introduced. Generally, the medical image objects (i.e., kidneys) contain the routine shape, size, and brightness which help the deterministic models for efficient segmentation. In the case of chronic diseases such ascancer, the objects in medical images contain tumors, which appear with a high degree of uncertainty in object properties. Due to the uncertainty in object shape, size, and brightness, former deep learning models performed less accurately in diseased object segmentation. In this paper, we proposed a hybrid semantic segmentation model with stochastic feature mapping techniques for the accurate segmentation of medical image objects underuncertainty. The location-dependent split method is used for seeded region marking and approximating object location. Stochastic neural networks with feature mapping techniques are introduced to localize the target objectswithout deterministic modeling. The recursive block-wide segmentation process is used to lineate the target objects from boundary elements. We tested the proposed stochastic segmentation model and its deep learning counterparts on a human kidney tumor MRI dataset. The experimental results show that the proposed stochastic segmentation model outperformed the segmentation of diseased kidney tumor objects with high accuracy and reliability.

Keywords
Stochastic Feature Mapping, Block Segmentation, Kidney Object Segmentation, Deep Neural Networks, Medical Image Analysis.

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