Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

Computer aided technology is used in biomedical image processing. In biomedical analysis features are extracted and then the proposed method will detect any abnormalities present or not in the system to be considered. In recent days the detection of brain tumour through image processing is made in medical diagnosis. The separation of tumor is made by the process of segmentation. Brain in human is the most complicated and delicate anatomical structure. There are various brain ailments in human but the indication of cancer in brain tumour may be fatal for the human. Brain tumor can be malignant or benign. The neurologist or neurosurgeon wants to know the exact location, size, shape and texture of tumor from Magnetic Resonance Imaging (MRI) of brain before going to the operation of the brain tumour or decided whether operation of removing brain tumour is at all necessary or not. The disease is analyzed since operation may cause death to the patient. Initially they took a chance by prescribing medicines to see whether there is any improvement of the condition of the patient. If the result is not satisfactory then there is no option other than operation of the tumor. Doctors also take an attempt to find the texture of the tumor since it may help them to know the progress of the tumour. In addition to Brain tumor segmentation, the detection of surface of the texture of brain tumor is required for proper treatment. The chapter proposed methods for detection of the progressive nature of the texture in the tumor presence in brain. For this process segmentation of tumor from other parts of brain is essential. In the chapter segmentation techniques are presented before the texture analysis process is given. Finally, comparisons of the proposed method with other methods are analyzed.

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Biomedical Image Processing Based on Regression Models

IFMBE Proceedings - 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics ◽

10.1007/978-3-540-69367-3_143 ◽

2008 ◽

pp. 536-539 ◽

Cited By ~ 1

Author(s):

A. Lorencs ◽

I. Mednieks ◽

Juris Sinica-Sinavskis

Keyword(s):

Image Processing ◽

Regression Models ◽

Biomedical Image Processing ◽

Biomedical Image

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Biomedical Image Processing Overview

Advances in Bioinformatics and Biomedical Engineering - Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes ◽

10.4018/978-1-4666-8811-7.ch001 ◽

2016 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Monia Mannai Mannai ◽

Wahiba Ben Abdessalem Karâa

Keyword(s):

Image Processing ◽

Medical Imaging ◽

Medical Image Processing ◽

Main Process ◽

Nuclear Medicine Imaging ◽

Biomedical Image Processing ◽

Biomedical Image ◽

X Ray Computed ◽

Magnetic Resonance Imaging Mri ◽

High Level

Over the years, there are different sorts of medical imaging have been developed. Where the most known are: X-ray, computed tomography (CT), nuclear medicine imaging (PET, SPECT), ultrasound and magnetic resonance imaging (MRI), each one has its different utilities. Various studies in biomedical informatics present a process to analyze images for extracting the hidden information which can be used after that. Image analysis combines several fields that are classified into two categories; the process of low-level, that requires very little information about the content image and the process of high-level, which may need information about the image content. The topic of this chapter is to introduce the different techniques for medical image processing and to present many research studies in this domain. It includes four stages, firstly, we introduce the most important medical imaging modalities and secondly, we outline the main process of biomedical image.

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