Literature DB >> 28512610

Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification.

T Y Satheesha1, D Satyanarayana2, M N Giri Prasad3, Kashyap D Dhruve4.   

Abstract

Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized dermoscopy system that considers the estimated depth of skin lesions for diagnosis. A 3-D skin lesion reconstruction technique using the estimated depth obtained from regular dermoscopic images is presented. On basis of the 3-D reconstruction, depth and 3-D shape features are extracted. In addition to 3-D features, regular color, texture, and 2-D shape features are also extracted. Feature extraction is critical to achieve accurate results. Apart from melanoma, in-situ melanoma the proposed system is designed to diagnose basal cell carcinoma, blue nevus, dermatofibroma, haemangioma, seborrhoeic keratosis, and normal mole lesions. For experimental evaluations, the PH2, ISIC: Melanoma Project, and ATLAS dermoscopy data sets is considered. Different feature set combinations is considered and performance is evaluated. Significant performance improvement is reported the post inclusion of estimated depth and 3-D features. The good classification scores of sensitivity = 96%, specificity = 97% on PH2 data set and sensitivity = 98%, specificity = 99% on the ATLAS data set is achieved. Experiments conducted to estimate tumor depth from 3-D lesion reconstruction is presented. Experimental results achieved prove that the proposed computerized dermoscopy system is efficient and can be used to diagnose varied skin lesion dermoscopy images.

Entities:  

Keywords:  3D features and tumor depth estimation; 3D lesion reconstruction; Melanoma in-situ; classification; skin lesions

Year:  2017        PMID: 28512610      PMCID: PMC5431259          DOI: 10.1109/JTEHM.2017.2648797

Source DB:  PubMed          Journal:  IEEE J Transl Eng Health Med        ISSN: 2168-2372            Impact factor:   3.316


  28 in total

1.  Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model.

Authors:  Marcelo Pereyra; Nicolas Dobigeon; Hadj Batatia; Jean-Yves Tourneret
Journal:  IEEE Trans Med Imaging       Date:  2012-03-12       Impact factor: 10.048

2.  Dermoscopic features of thin melanomas: a comparative study of melanoma in situ and invasive melanomas smaller than or equal to 1mm.

Authors:  Vanessa Priscilla Martins da Silva; Juliana Kida Ikino; Mariana Mazzochi Sens; Daniel Holthausen Nunes; Gabriella Di Giunta
Journal:  An Bras Dermatol       Date:  2013 Sep-Oct       Impact factor: 1.896

3.  Fine mapping of tissue properties on excised samples of melanoma and skin without the need for histological staining.

Authors:  Bernhard R Tittmann; Chiaki Miyasaka; Elena Maeva; David Shum
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2013-02       Impact factor: 2.725

4.  Modified ABC-point list of dermoscopy: A simplified and highly accurate dermoscopic algorithm for the diagnosis of cutaneous melanocytic lesions.

Authors:  Andreas Blum; Gernot Rassner; Claus Garbe
Journal:  J Am Acad Dermatol       Date:  2003-05       Impact factor: 11.527

5.  Melanoma detection algorithm based on feature fusion.

Authors:  Catarina Barata; M Emre Celebi; Jorge S Marques
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2015-08

6.  Detection and analysis of irregular streaks in dermoscopic images of skin lesions.

Authors:  Maryam Sadeghi; Tim K Lee; David McLean; Harvey Lui; M Stella Atkins
Journal:  IEEE Trans Med Imaging       Date:  2013-01-14       Impact factor: 10.048

7.  Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis.

Authors:  G Argenziano; G Fabbrocini; P Carli; V De Giorgi; E Sammarco; M Delfino
Journal:  Arch Dermatol       Date:  1998-12

8.  Neural network diagnosis of malignant melanoma from color images.

Authors:  F Ercal; A Chawla; W V Stoecker; H C Lee; R H Moss
Journal:  IEEE Trans Biomed Eng       Date:  1994-09       Impact factor: 4.538

Review 9.  Overview of advanced computer vision systems for skin lesions characterization.

Authors:  Ilias Maglogiannis; Charalampos N Doukas
Journal:  IEEE Trans Inf Technol Biomed       Date:  2009-03-16

10.  Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention.

Authors:  Omar Abuzaghleh; Buket D Barkana; Miad Faezipour
Journal:  IEEE J Transl Eng Health Med       Date:  2015-04-03       Impact factor: 3.316

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  4 in total

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Authors:  D Roja Ramani; S Siva Ranjani
Journal:  J Med Syst       Date:  2019-06-12       Impact factor: 4.460

2.  Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning.

Authors:  Priyanka Vasanthakumari; Renan A Romano; Ramon G T Rosa; Ana G Salvio; Vladislav Yakovlev; Cristina Kurachi; Jason M Hirshburg; Javier A Jo
Journal:  J Biomed Opt       Date:  2022-06       Impact factor: 3.758

3.  An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification.

Authors:  M Attique Khan; Tallha Akram; Muhammad Sharif; Aamir Shahzad; Khursheed Aurangzeb; Musaed Alhussein; Syed Irtaza Haider; Abdualziz Altamrah
Journal:  BMC Cancer       Date:  2018-06-05       Impact factor: 4.430

Review 4.  Computational models of melanoma.

Authors:  Marco Albrecht; Philippe Lucarelli; Dagmar Kulms; Thomas Sauter
Journal:  Theor Biol Med Model       Date:  2020-05-14       Impact factor: 2.432

  4 in total

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