Detecting and Measuring Surface Area of Skin Lesions

Mirzaalian-Dastjerdi, Houman; Töpfer, Dominique; Bangemann, Michael; Maier, Andreas

doi:10.1007/978-3-662-56537-7_20

Cited by 3 publications

(5 citation statements)

References 7 publications

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“…The segmentation method was already described in [13] and as here no new results are reported, it shall only be explained shortly for completeness. It is based on Random Forest (RF) classification [15].…”

Section: Methodsmentioning

confidence: 99%

“…It is based on Random Forest (RF) classification [15]. The RF is trained such that it classifies images into only two groups, wound and skin [13]. Therefore, the output of the RF for background or ruler pixels is not meaningful and they are discarded using a Region of Interest (ROI) defined by the user by roughly drawing a contour around the lesion; see Figure 1(a).…”

Section: Methodsmentioning

confidence: 99%

“…Considering all above-mentioned limitations of existing methods, this work aims to provide an easy, convenient, fast, and low-cost approach for semi-automatic wound segmentation and area measurement with high accuracy based on a single image. As the details of the segmentation method have already been described in [13], only a brief presentation of the method's design will be given here, to focus particularly on the details of surface area measurement algorithms. The input image contains a flexible paper ruler to estimate the area of a lesion on a curved surface.…”

Section: Introductionmentioning

confidence: 99%

“…However, time pressure is usually a problem in clinical routine and both manual delineation and taking multiple images from different angles can become time-consuming when it has to be done for many patients. In experiments, the accuracy of this method is evaluated for wound segmentation and area measurement [13]. Zenteno et al [14] have compared VisualSFM and the laser scanner approach.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Surface Area of Skin Lesions with 2D and 3D Algorithms

Dastjerdi

Töpfer²,

Rupitsch

et al. 2019

International Journal of Biomedical Imaging

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Purpose The treatment of skin lesions of various kinds is a common task in clinical routine. Apart from wound care, the assessment of treatment efficacy plays an important role. In this paper, we present a new approach to measure the skin lesion surface in two and three dimensions. Methods For the 2D approach, a single photo containing a flexible paper ruler is taken. After semi-automatic segmentation of the lesion, evaluation is based on local scale estimation using the ruler. For the 3D approach, reconstruction is based on Structure from Motion. Roughly outlining the region of interest around the lesion is required for both methods. Results The measurement evaluation was performed on 117 phantom images and five phantom videos for 2D and 3D approach, respectively. We found an absolute error of 0.99±1.18 cm2 and a relative error 9.89± 9.31% for 2D. These errors are <1 cm2 and <5% for five test phantoms in our 3D case. As expected, the error of 2D surface area measurement increased by approximately 10% for wounds on the bent surface compared to wounds on the flat surface. Using our method, the only user interaction is to roughly outline the region of interest around the lesion. Conclusions We developed a new wound segmentation and surface area measurement technique for skin lesions even on a bent surface. The 2D technique provides the user with a fast, user-friendly segmentation and measurement tool with reasonable accuracy for home care assessment of treatment. For 3D only preliminary results could be provided. Measurements were only based on phantoms and have to be repeated with real clinical data.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring Surface Area of Skin Lesions with 2D and 3D Algorithms

Dastjerdi

Töpfer²,

Rupitsch

et al. 2019

International Journal of Biomedical Imaging

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“…Segmentation is an essential initial step, for CAD of skin lesions [2] and melanoma in particular. This is because melanoma is typically diagnosed based on the 'ABCD' criterion, which takes into account the shape-characteristics of lesions (such as diameter, asymmetry, border irregularity, etc.…”

Section: Introductionmentioning

confidence: 99%

A Multi-task Framework for Skin Lesion Detection and Segmentation

Vesal

Patil

Ravikumar

et al. 2018

Lecture Notes in Computer Science

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Early detection and segmentation of skin lesions is crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients. However, manual delineation is time consuming and subject to intra-and inter-observer variations among dermatologists. This underlines the need for an accurate and automatic approach to skin lesion segmentation. To tackle this issue, we propose a multi-task convolutional neural network (CNN) based, joint detection and segmentation framework, designed to initially localize the lesion and subsequently, segment it. A 'Faster region-based convolutional neural network' (Faster-RCNN) which comprises a region proposal network (RPN), is used to generate bounding boxes/region proposals, for lesion localization in each image. The proposed regions are subsequently refined using a softmax classifier and a bounding-box regressor. The refined bounding boxes are finally cropped and segmented using 'SkinNet', a modified version of U-Net. We trained and evaluated the performance of our network, using the ISBI 2017 challenge and the PH2 datasets, and compared it with the state-of-the-art, using the official test data released as part of the challenge for the former. Our approach outperformed others in terms of Dice coefficients (> 0.93), Jaccard index (> 0.88), accuracy (> 0.96) and sensitivity (> 0.95), across five-fold cross validation experiments.Recent trends indicate a growing number of skin cancer diagnoses worldwide, each year. In 2016, approximately 80,000 new cases of skin cancer were expected to be diagnosed, with 10,000 melanoma related deaths (the most aggressive form of skin cancer), in the USA alone [1]. Clinical screening and diagnosis typically involve examination by an expert dermatologist, followed by histopathological analysis of biopsies. These steps however, invariably suffer from high inter-rater and inter-center variability, and studies have shown that patient survival rates improve to over 95%, following early detection and diagnosis of melanomas. To

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