Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

Roobaea Alroobaea,Saeed Rubaiee,Sami Bourouis,Anas Ahmed

doi:10.3390/info11030155

Roobaea Alroobaea, Saeed Rubaiee + Show 2 more

Open Access

https://doi.org/10.3390/info11030155

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Abstract

Accurate medical images analysis plays a vital role for several clinical applications. Nevertheless, the immense and complex data volume to be processed make difficult the design of effective algorithms. The first aim of this paper is to examine this area of research and to provide some relevant reference sources related to the context of medical image analysis. Then, an effective hybrid solution to further improve the expected results is proposed here. It allows to consider the benefits of the cooperation of different complementary approaches such as statistical-based, variational-based and atlas-based techniques and to reduce their drawbacks. In particular, a pipeline framework that involves different steps such as a preprocessing step, a classification step and a refinement step with variational-based method is developed to identify accurately pathological regions in biomedical images. The preprocessing step has the role to remove noise and improve the quality of the images. Then the classification is based on both symmetry axis detection step and non linear learning with SVM algorithm. Finally, a level set-based model is performed to refine the boundary detection of the region of interest. In this work we will show that an accurate initialization step could enhance final performances. Some obtained results are exposed which are related to the challenging application of brain tumor segmentation.

Highlights

Medical Image Analysis ChallengesPrecise analysis of medical images such as the segmentation, the detection and the quantification of tumors and cancers are an important task for many clinical applications including medical content-based image retrieval, 3D pathology modelling, normal and abnormal templates construction, diagnosis, and therapy evaluation [1,2,3,4]
To overcome some existing limitations, in this research, we focus on the development of an effective hybrid framework for a specific task which is known as image segmentation
We have reviewed specific relevant works related to the area of medical image analysis

Summary

Introduction

Precise analysis of medical images such as the segmentation, the detection and the quantification of tumors and cancers are an important task for many clinical applications including medical content-based image retrieval, 3D pathology modelling, normal and abnormal templates (atlases) construction, diagnosis, and therapy evaluation [1,2,3,4]. Several image processing-based techniques had been proposed in the literature and many of them play a vital role in the medical imaging applications. Many of these approaches are Information 2020, 11, 155; doi:10.3390/info11030155 www.mdpi.com/journal/information. To overcome some existing limitations, in this research, we focus on the development of an effective hybrid framework for a specific task which is known as image segmentation. Note that due to the impossibility of an exhaustive review of all segmentation method in a single article, we restricted ourselves to present only some relevant approaches related to medical image analysis.

Atlas-Guided Methods

Variational Deformable Models

Statistical Classification and Segmentation of Medical Images

A Unified Framework for Brain Tumor Segmentation

Findings

Conclusions and Discussion