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

2019 ◽  
Author(s):  
Minakshi Waghulde ◽  
Shirish Kulkarni ◽  
Gargi Phadke
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Digital Images ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Skin cancer detection and stage prediction using image processing techniques

2018 ◽  
Vol 7 (1.8) ◽  
pp. 204 ◽  
Author(s):  
Sheeju Diana ◽  
Ramamurthy B
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Noise Removal ◽  
The Body ◽  
Back Propagation Algorithm ◽  
Environmental Threats ◽  
Image Processing Techniques ◽  
Processing Techniques

Skin cancer is one of the perilous forms of cancer that most recently occurred in preceding and in recent years as well. Early detection of skin cancer is curable and it eliminates the cost that is spent on the advanced treatment. Skin cancer mainly occurs due to exposure to sun’s ultraviolet radiation and other environmental threats. It can be categorized into, Melanoma and Non-Melanoma. Melanoma is dangerous one. Once it is occurred it starts spreading across other parts of the body if not treated in the early stages. Non-Melanoma is a static cancer which does not affect the normal cells of the skin. This paper aims to develop an application to detect skin cancer and stage prediction using Image Processing Techniques. Stage is predicted, so that the treatment for the same is done without any delay. Skin cancer affected image is taken as input and various preprocessing techniques is applied for the same. The Preprocessing Techniques such as Noise Removal is applied on the image to filter out the noise. Filtered image is enhanced using Histogram Equalization and image is segmented to extract the affected portion. The Area, Perimeter and Eccentricity values are calculated for the affected portion of the skin. The values are then fed into the Neural Networks using Back Propagation algorithm in order to predict the Stage and type of the Skin cancer.

scholarly journals Skin Cancer Analysis with Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 2663-2672
Author(s):  
Siddharth Raj Dash
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Skin Cancer ◽  
Medical Diagnosis ◽  
Skin Diseases ◽  
Detection System ◽  
Technological Advancement ◽  
Image Processing Techniques ◽  
The Cost ◽  
Processing Techniques

Skin diseases are some of the most common diseases and are often difficult to diagnose than other diseases. Skin diseases may be caused by fungus, bacteria, allergic reaction, viruses, cancer etc. The technological advancement in laser diagnosis and Photonics based medical diagnosis has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of diagnostics is time-consuming and very expensive. Hence, we can use image processing techniques to help build automated preliminary detection system for such dermatological diagnostics.

Quantum Computation Perspectives in Medical Image Processing

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 ◽  
Image Processing Techniques ◽  
Processing Techniques

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.

Digital Image Analysis for Early Diagnosis of Cancer

2019 ◽  
pp. 69-102
Author(s):  
Durjoy Majumder ◽  
Madhumita Das
Keyword(s):  
Image Processing ◽  
Digital Images ◽  
Qualitative Assessment ◽  
Background Information ◽  
Image Processing Techniques ◽  
Early Diagnosis Of Cancer ◽  
Histopathological Images ◽  
Characteristic Features ◽  
Cancer Biopsy ◽  
Processing Techniques

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.

APPLICATION OF IMAGE PROCESSING TECHNIQUES FOR MOLD COLONY GROWTH TRACKING

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 ◽  
Image Processing Techniques ◽  
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

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.

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

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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