Digital Image Analysis for Early Diagnosis of Cancer

Early Diagnosis Of Cancer ◽

Histopathological Images ◽

Characteristic Features ◽

Cancer Biopsy ◽

Cancer diagnoses so far are based on pathologists' criteria. Hence, they are based on qualitative assessment. Histopathological images of cancer biopsy samples are now available in digital format. Such digital images are now gaining importance. To avoid individual pathologists' qualitative assessment, digital images are processed further through use of computational algorithm. To extract characteristic features from the digital images in quantitative terms, different techniques of mathematical morphology are in use. Recently several other statistical and machine learning techniques have developed to classify histopathological images with the pathologists' criteria. Here, the authors discuss some characteristic features of image processing techniques along with the different advanced analytical methods used in oncology. Relevant background information of these techniques are also elaborated and the recent applications of different image processing techniques for the early detection of cancer are also discussed.

Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review

Journal of Medical Systems ◽

10.1007/s10916-021-01786-9 ◽

2021 ◽

Vol 46 (1) ◽

Author(s):

R Rashmi ◽

Keerthana Prasad ◽

Chethana Babu K Udupa

Keyword(s):

Breast Cancer ◽

Image Processing ◽

Medical Image ◽

Human Life ◽

Medical Image Processing ◽

Histopathological Analysis ◽

Computer Aided ◽

Histopathological Images ◽

AbstractBreast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.

Detection of Skin Cancer Lesions from Digital Images with Image Processing Techniques

2019 IEEE Pune Section International Conference (PuneCon) ◽

10.1109/punecon46936.2019.9105886 ◽

2019 ◽

Author(s):

Minakshi Waghulde ◽

Shirish Kulkarni ◽

Gargi Phadke

Keyword(s):

Image Processing ◽

Skin Cancer ◽

Digital Images ◽

Quantum Computation Perspectives in Medical Image Processing

International Journal of Nanotechnology and Molecular Computation ◽

10.4018/978-1-61520-670-4.ch006 ◽

2010 ◽

Vol 2 (2) ◽

pp. 16-46

Author(s):

Pedro Rodrigues ◽

Manuel João Ferreira ◽

João Luís Monteiro

Keyword(s):

Image Processing ◽

Quantum Computation ◽

Computational Methods ◽

Medical Image ◽

Functional Performance ◽

Digital Images ◽

Medical Image Processing ◽

Quantum Devices ◽

The need to increase the complexity of computational methods to produce improvements in functional performance, particularly in medical image processing applications, leads to find suitable physical devices. This chapter describes two ways of adapting the techniques of image processing to quantum devices. This kind of computing can achieve, for some problems, unparalleled performance as compared to classic computing. In the first method, using the quantum Grover’s algorithm how to implement image processing techniques under quantum rules is shown. In the second method, using diffraction and interference, the possibility of using less complex quantum devices for processing digital images is treated. Using leucocytes images, that mode is tested.

APPLICATION OF IMAGE PROCESSING TECHNIQUES FOR MOLD COLONY GROWTH TRACKING

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2018.08.001 ◽

2018 ◽

pp. 300-309

Author(s):

Rositsa Yordanova ◽

Petya Nikolova ◽

Stanka Baycheva

Keyword(s):

Image Processing ◽

Mathematical Models ◽

Digital Images ◽

Colony Growth ◽

Colony Development ◽

Nutrient Media ◽

Processing Techniques ◽

Selection Of ◽

Color Components

The report analyses an algorithm to track the growth of colonies from mold by digital images. The effect of color components on the recognition and enumeration of mold colonies was analyzed. A selection of form description coefficients has been made to trace this growth. The nature of the modification of the colony of mold in two nutrient media has been established. The obtained data can be used to develop mathematical models describing colony development. Connections and comparisons can be made of the process that influence development of molds.

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 ◽

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.

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

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

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 ◽

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.