<title>Maximum detection range of low-intensity target edges as a function of variable albedo and precipitation using morphological and segmentation image processing techniques</title>

Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.

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A Study on Image Segmentation Method for Image Processing

10.3233/apc210223 ◽

2021 ◽

Author(s):

S. Prabu ◽

J.M. Gnanasekar

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Surveillance System ◽

Vital Role ◽

Important Process ◽

Image Processing Techniques ◽

Segmentation Methods ◽

And Performance ◽

Segmentation Image ◽

Processing Techniques

Image processing techniques are essential part of the current computer technologies and that it plays vital role in various applications like medical field, object detection, video surveillance system, computer vision etc. The important process of Image processing is Image Segmentation. Image Segmentation is the process of splitting the images into various tiny parts called segments. Image processing makes to simplify the image representation in order to analyze the images. So many algorithms are developed for segmenting images, based on the certain feature of the pixel. In this paper different algorithms of segmentation can be reviewed, analyzed and finally list out the comparison for all the algorithms. This comparison study is useful for increasing accuracy and performance of segmentation methods in various image processing domains.

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Three-Dimensional Structure of Rotavirus-Fab Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179944 ◽

1990 ◽

Vol 48 (1) ◽

pp. 238-239

Author(s):

B.V.V. Prasad ◽

E. Marietta ◽

J.W. Burns ◽

M.K. Estes ◽

W. Chiu

Keyword(s):

Image Processing ◽

Smooth Surface ◽

Three Dimensional ◽

Outer Shell ◽

Dimensional Structure ◽

Structural Studies ◽

Cell Penetration ◽

Electron Cryomicroscopy ◽

Image Processing Techniques ◽

Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

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A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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