Automatic Fitting of Feature Points for Border Detection of Skin Lesions in Medical Images with Bat Algorithm

2018 ◽  
pp. 357-368 ◽  
Author(s):  
Akemi Gálvez ◽  
Iztok Fister ◽  
Iztok Fister ◽  
Eneko Osaba ◽  
Javier Del Ser ◽  
...  
Keyword(s):  
Medical Images ◽  
Bat Algorithm ◽  
Skin Lesions ◽  
Feature Points ◽  
Border Detection ◽  
Automatic Fitting

Analysis of Valuable Techniques and Algorithms Used in Automated Skin Lesion Recognition Systems

Oncology ◽  
2017 ◽  
pp. 542-558
Author(s):  
Uzma Jamil ◽  
Shehzad Khalid
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Skin Lesions ◽  
Lesion Detection ◽  
Diagnostic Systems ◽  
Border Detection ◽  
Data Process ◽  
Pros And Cons ◽  
Recognition Systems

Application of computational intelligence techniques helps physicians as well as dermatologists in faster data process to give better and more reliable diagnoses. The whole system is categorized as: Pre-processing the lesion image to enhance its readability, Segmentation of the Lesion from skin, Feature extraction, selection, and finally the identification of dermoscopic images. Pros and cons of various methods are focused to provide a help for the researchers starting work in automated lesion detection system. Numerous computerized diagnostic systems have been reported in which different border detection, feature extraction, selection, and classification algorithms are used. The aim of this review is to summarize and compare advanced dermoscopic algorithms used for the classification of skin lesions and discuss important issues affecting the success of classification. This paper will be a guide that represents a comprehensive guideline for selecting suitable algorithms needed for different steps of automatic diagnostic procedure for ensuring timely diagnosis of skin cancer.

Classification of Medical Images in the Domain of Melanoid Skin Lesions

2008 ◽  
pp. 519-526 ◽  
Author(s):  
Zdzislaw S. Hippe ◽  
Jerzy Grzymala-Busse ◽  
Piotr Blajdo ◽  
Maksymilian Knap ◽  
Teresa Mroczek ◽  
...  
Keyword(s):  
Medical Images ◽  
Skin Lesions

SEGMENTATION OF DERMATOSCOPIC IMAGES USED FOR COMPUTER-AIDED DIAGNOSIS OF MELANOMA

2010 ◽  
Vol 10 (02) ◽  
pp. 213-223
Author(s):  
MHAMMED MESSADI ◽  
ABDELHAFID BESSAID ◽  
A. TALEB-AHMED
Keyword(s):  
Methodological Approach ◽  
Region Growing ◽  
Skin Lesions ◽  
Skin Tumor ◽  
Border Detection ◽  
Fast Approach ◽  
Malignant Skin ◽  
Pigmented Skin Lesions ◽  
Aided Diagnosis ◽  
Related Mortality

In this paper, a methodological approach to the segmentation of tumours skin lesions in dermoscopy images is presented. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly, its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. In dermatoscopic images, the segmentation is essential to characterize the information shape of the lesion and also to locate the tumor for analysis. In this domain, we have evaluated several techniques for the segmentation of dermatoscopic images. All these methods do not exactly separate the lesion from the background. In this work a fast approach in border detection of dermoscopy pigmented skin lesions images based on the region growing algorithm is presented. This method is tested on a set of 60 dermoscopy images. The obtained results show that the presented method achieves both fast and accurate border detection.

Analysis of Valuable Techniques and Algorithms Used in Automated Skin Lesion Recognition Systems

2015 ◽  
Vol 3 (2) ◽  
pp. 92-107 ◽  
Author(s):  
Uzma Jamil ◽  
Shehzad Khalid
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Skin Lesions ◽  
Lesion Detection ◽  
Diagnostic Systems ◽  
Border Detection ◽  
Data Process ◽  
Pros And Cons ◽  
Recognition Systems

Application of computational intelligence techniques helps physicians as well as dermatologists in faster data process to give better and more reliable diagnoses. The whole system is categorized as: Pre-processing the lesion image to enhance its readability, Segmentation of the Lesion from skin, Feature extraction, selection, and finally the identification of dermoscopic images. Pros and cons of various methods are focused to provide a help for the researchers starting work in automated lesion detection system. Numerous computerized diagnostic systems have been reported in which different border detection, feature extraction, selection, and classification algorithms are used. The aim of this review is to summarize and compare advanced dermoscopic algorithms used for the classification of skin lesions and discuss important issues affecting the success of classification. This paper will be a guide that represents a comprehensive guideline for selecting suitable algorithms needed for different steps of automatic diagnostic procedure for ensuring timely diagnosis of skin cancer.

Analysis of Valuable Techniques and Algorithms Used in Automated Skin Lesion Recognition Systems

2017 ◽  
pp. 1327-1342
Author(s):  
Uzma Jamil ◽  
Shehzad Khalid
Keyword(s):  
Feature Extraction ◽  
Detection System ◽  
Skin Lesions ◽  
Lesion Detection ◽  
Diagnostic Systems ◽  
Border Detection ◽  
Data Process ◽  
Pros And Cons ◽  
Recognition Systems

Application of computational intelligence techniques helps physicians as well as dermatologists in faster data process to give better and more reliable diagnoses. The whole system is categorized as: Pre-processing the lesion image to enhance its readability, Segmentation of the Lesion from skin, Feature extraction, selection, and finally the identification of dermoscopic images. Pros and cons of various methods are focused to provide a help for the researchers starting work in automated lesion detection system. Numerous computerized diagnostic systems have been reported in which different border detection, feature extraction, selection, and classification algorithms are used. The aim of this review is to summarize and compare advanced dermoscopic algorithms used for the classification of skin lesions and discuss important issues affecting the success of classification. This paper will be a guide that represents a comprehensive guideline for selecting suitable algorithms needed for different steps of automatic diagnostic procedure for ensuring timely diagnosis of skin cancer.

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