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.

Detection and Classification of Skin Lesions using Probability Map-based Region Growing with BA-KNN Classifier (Preprint)

2021 ◽  
Author(s):  
Sivaraj S ◽  
Dr.R. Malmathanraj
Keyword(s):  
Skin Lesion ◽  
Region Growing ◽  
Skin Lesions ◽  
Classification Approach ◽  
Probability Map ◽  
Knn Classifier ◽  
Knn Classification ◽  
Pigmented Skin Lesions ◽  
Thresholding Algorithm

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

scholarly journals Towards a computer-aided diagnosis system for pigmented skin lesions

2003 ◽  
Vol 27 (1) ◽  
pp. 65-78 ◽  
Author(s):  
Philippe Schmid-Saugeona ◽  
Joël Guillodb ◽  
Jean-Philippe Thirana,
Keyword(s):  
Skin Lesions ◽  
Computer Aided Diagnosis ◽  
Diagnosis System ◽  
Computer Aided ◽  
Pigmented Skin Lesions ◽  
Aided Diagnosis

scholarly journals A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

2021 ◽  
Vol 14 (3) ◽  
pp. 1231-1247
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions ◽  
Learning Framework ◽  
Melanoma Detection ◽  
Pigmented Skin Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

NEW CHARACTERIZATION METHODOLOGY FOR SKIN TUMORS CLASSIFICATION

2010 ◽  
Vol 10 (03) ◽  
pp. 467-477 ◽  
Author(s):  
M. MESSADI ◽  
A. BESSAID ◽  
A. TALEB-AHMED
Keyword(s):  
Neural Network ◽  
Benign Tumor ◽  
Malignant Tumors ◽  
Skin Tumors ◽  
Skin Tumor ◽  
Benign Lesions ◽  
Malignant Skin ◽  
Textural Parameters ◽  
Tumors Classification ◽  
Related Mortality

Our objective in this paper is to introduce the efficacies of texture in the interpretation of color skin images. 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. Using the features of skin tumors, such as color, symmetry, and border regularity, an attempt is made to determinate if the skin tumor is a melanoma or a benign tumor. In this work, we are interested by adding to form parameters such as the asymmetry (A) and the shape irregularities of skin tumors (B), the textural parameters to estimate colors in dermatoscopic images. In this case, the images are analyzed using textural parameters computed in several directions. These parameters and the form parameters are added to obtain a better classification results. A statistical analysis is performed over these ratios to select the most highly discriminating textural parameters. The method has been tested successfully on 144 images and we found significant differences between the lesions (melanoma and benign). Finally, these parameters (form and parameters of texture selected) are only use to classify the benign and malignancy of the skin lesion. A multilayer neural network is employed to differentiate between malignant tumors and benign lesions.

Special Considerations In The Management Of Malignant Skin Lesions About The Eye

1993 ◽  
Vol 26 (2) ◽  
pp. 215-230
Author(s):  
Gerry F. Funk ◽  
Henry T. Hoffman ◽  
Keith D. Carter
Keyword(s):  
Skin Lesions ◽  
Malignant Skin

The use of Integra dermal regeneration matrix in the surgical treatment of a recurrent, extensive malignant skin lesion in the temporal region

2013 ◽  
Vol 154 (6) ◽  
pp. 225-227 ◽  
Author(s):  
Csaba Halmy ◽  
Zoltán Nádai ◽  
Krisztián Csőre ◽  
Adrienne Vajda ◽  
Róbert Tamás
Keyword(s):  
Surgical Treatment ◽  
Skin Lesion ◽  
Surgical Management ◽  
Skin Grafting ◽  
Skin Tumor ◽  
Temporal Region ◽  
Aesthetic Result ◽  
Malignant Skin ◽  
Dermal Regeneration Template ◽  
Dermal Regeneration

Authors report on the use of Integra dermal regeneration template after excision of an extended, recurrent skin tumor in the temporal region. The area covered with Integra was 180 cm2. Skin grafting to cover Integra was performed on the 28th day. Both Integra and the skin transplant were taken 100%. Integra dermal regeneration template can provide good functional and aesthetic result in the surgical management of extended skin tumors over the skull. Orv. Hetil., 2013, 154, 225–227.

Current Progresses of Functional Nanomaterials for Imaging Diagnosis and Treatment of Melanoma

2019 ◽  
Vol 19 (27) ◽  
pp. 2494-2506 ◽  
Author(s):  
Congcong Zhu ◽  
Yunjie Zhu ◽  
Huijun Pan ◽  
Zhongjian Chen ◽  
Quangang Zhu
Keyword(s):  
Treatment Options ◽  
Skin Tumor ◽  
Diagnosis And Treatment ◽  
Chemotherapeutic Drugs ◽  
Imaging Diagnosis ◽  
Functional Nanomaterials ◽  
Disease Prognosis ◽  
Unsuccessful Treatment ◽  
Short Period ◽  
Malignant Skin

Melanoma is a malignant skin tumor that results in poor disease prognosis due to unsuccessful treatment options. During the early stages of tumor progression, surgery is the primary approach that assures a good outcome. However, in the presence of metastasis, melanoma hasbecome almost immedicable, since the tumors can not be removed and the disease recurs easily in a short period of time. However, in recent years, the combination of nanomedicine and chemotherapeutic drugs has offered promising solutions to the treatment of late-stage melanoma. Extensive studies have demonstrated that nanomaterials and their advanced applications can improve the efficacy of traditional chemotherapeutic drugs in order to overcome the disadvantages, such as drug resistance, low drug delivery rate and reduced targeting to the tumor tissue. In the present review, we summarized the latest progress in imaging diagnosis and treatment of melanoma using functional nanomaterials, including polymers, liposomes, metal nanoparticles, magnetic nanoparticles and carbon-based nanoparticles. These nanoparticles are reported widely in melanoma chemotherapy, gene therapy, immunotherapy, photodynamic therapy, and hyperthermia.

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