Literature DB >> 24081839

Simpler, faster, more accurate melanocytic lesion segmentation through MEDS.

Francesco Peruch, Federica Bogo, Michele Bonazza, Vincenzo-Maria Cappelleri, Enoch Peserico.   

Abstract

We present a new technique for melanocytic lesion segmentation, Mimicking Expert Dermatologists' Segmentations (MEDS), and extensive tests of its accuracy, speed, and robustness. MEDS combines a thresholding scheme reproducing the cognitive process of dermatologists with a number of optimizations that may be of independent interest. MEDS is simple, with a single parameter tuning its “tightness”. It is extremely fast, segmenting medium-resolution images in a fraction of a second even with the modest computational resources of a cell phone-an improvement of an order of magnitude or more over state-of-the-art techniques. And it is extremely accurate: very experienced dermatologists disagree with its segmentations less than they disagree with the segmentations of state-of-the-art techniques, and in fact less than they disagree with the segmentations of dermatologists of moderate experience.

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Year:  2014        PMID: 24081839     DOI: 10.1109/TBME.2013.2283803

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  6 in total

1.  An Efficient Melanoma Diagnosis Approach Using Integrated HMF Multi-Atlas Map Based Segmentation.

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

2.  Skin Lesion Segmentation with Improved Convolutional Neural Network.

Authors:  Şaban Öztürk; Umut Özkaya
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

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

Authors:  T Y Satheesha; D Satyanarayana; M N Giri Prasad; Kashyap D Dhruve
Journal:  IEEE J Transl Eng Health Med       Date:  2017-01-16       Impact factor: 3.316

4.  Density-based parallel skin lesion border detection with webCL.

Authors:  James Lemon; Sinan Kockara; Tansel Halic; Mutlu Mete
Journal:  BMC Bioinformatics       Date:  2015-09-25       Impact factor: 3.169

5.  Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

Authors:  Yuexiang Li; Linlin Shen
Journal:  Sensors (Basel)       Date:  2018-02-11       Impact factor: 3.576

6.  Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm.

Authors:  Halil Murat Ünver; Enes Ayan
Journal:  Diagnostics (Basel)       Date:  2019-07-10
  6 in total

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