Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Madooei, Ali; Drew, Mark S.

doi:10.1155/2016/4868305

Cited by 21 publications

(8 citation statements)

References 124 publications

(146 reference statements)

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“…To detect melanoma at an early stage, the most subtle colour shades (especially brown) and structures need to be identified by dermoscopy, often only in a small part of the lesion [58][59][60]. Colour assessment is essential for the clinical diagnosis of skin cancers [6,[25][26][27][28][29]. Therefore, dermatologists need a dermoscopy device able to provide an image with authentic colours and sharply defined tiny structures in the lesion, as well as an image that offers the same good quality throughout the whole dermoscopic field, including at magnification [6,8].…”

Section: Discussionmentioning

confidence: 99%

Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Dugonik

Marovt

et al. 2020

Applied Sciences

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The fast-growing incidence of skin cancer, especially melanoma, is the guiding principle for intense development of various digital image-capturing devices providing easier recognition of melanoma by dermatologists. Handheld and digital dermoscopy, following of mole changes with smartphones and digital analysing of mole images, is based on evaluation of the colours, shape and deep structures in the skin moles. Incorrect colour information of an image, under- or overexposed images, lack of sharpness and low resolution of the images, can lead to melanoma misdiagnosis. The purpose of our study was to determine the colour error in the image according to the given lighting conditions and different camera settings. We focused on measuring the image quality parameters of smartphones and high-resolution cameras to compare them with the results of state-of-the-art dermoscopy device systems. We applied standardised measuring methods. The spatial frequency response method was applied for measuring the sharpness and resolution of the tested camera systems. Colour images with known reference values were captured from the test target, to evaluate colour error as a CIELAB (Commission Internationale de l’Eclairage) ΔE*ab colour difference as seen by a human observer. The results of our measurements yielded two significant findings. First, all tested cameras produced inaccurate colours when operating in automatic mode, and second, the amount of sharpening was too intensive. These deficiencies can be eliminated through adjusting the camera parameters manually or by image post-production. The presented two-step camera calibration procedure improves the colour accuracy of captured clinical and dermoscopy images significantly.

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Section: Discussionmentioning

confidence: 99%

Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Dugonik

Marovt

et al. 2020

Applied Sciences

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“…Additional aspects that pose a challenge are the different colour calibrations of the cameras and different light sources, as illustrated in Fig 3 . Colour is one of the main clinical features for melanoma detection [ 5 ], and computer systems normally have one or more colour features [ 23 ]. There have been attempts at automatic colour calibration based on image content [ 24 ], but the impact on classification is not fully investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Different image resolutions, magnifications and distances to the skin result in the true size of the lesion differing from image to image, though this can be solved easily by providing information regarding pixels per mm. Additional aspects that pose a challenge are the different colour calibrations of the cameras and different light sources, as illustrated in Fig 3 . Colour is one of the main clinical features for melanoma detection [5], and computer systems normally have one or more colour features [23]. There have been attempts at automatic colour calibration based on image content [24], but the impact on classification is not fully investigated.…”

Section: Discussionmentioning

confidence: 99%

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images

et al. 2017

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Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

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“…By contrast, a computer aided diagnostic (CAD) system is more objective. By utilizing handcrafted features, traditional CAD systems for skin disease classification can achieve excellent performance in certain skin disease diagnosis tasks [11,12,13]. However, these systems usually focus on limited types of skin diseases, such as melanoma and BCC.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, deep learning methods have become popular in feature learning and achieved excellent performances in various tasks, including image classification [19,20], segmentation [21,22], object detection [23,24] and localization [25,26]. A variety of researches [9,23,12,27,25] showed that the deep learning methods were able to surpass humans in many computer vision tasks. One thing behind the success of deep learning is its ability to learn semantic features automatically from large-scale datasets.…”

Section: Introductionmentioning

confidence: 99%

Skin disease diagnosis with deep learning: a review

Li¹,

Pan²,

Zhao³

et al. 2020

Preprint

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Skin cancer is one of the most threatening diseases worldwide. However, diagnosing skin cancer correctly is challenging. Recently, deep learning algorithms have emerged to achieve excellent performance on various tasks. Particularly, they have been applied to the skin disease diagnosis tasks. In this paper, we present a review on deep learning methods and their applications in skin disease diagnosis. We first present a brief introduction to skin diseases and image acquisition methods in dermatology, and list several publicly available skin datasets for training and testing algorithms. Then, we introduce the conception of deep learning and review popular deep learning architectures. Thereafter, popular deep learning frameworks facilitating the implementation of deep learning algorithms and performance evaluation metrics are presented. As an important part of this article, we then review the literature involving deep learning methods for skin disease diagnosis from several aspects according to the specific tasks. Additionally, we discuss the challenges faced in the area and suggest possible future research directions. The major purpose of this article is to provide a conceptual and systematically review of the recent works on skin disease diagnosis with deep learning. Given the popularity of deep learning, there remains great challenges in the area, as well as opportunities that we can explore in the future.

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Incorporating Colour Information for Computer-Aided Diagnosis of Melanoma from Dermoscopy Images: A Retrospective Survey and Critical Analysis

Cited by 21 publications

References 124 publications

Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images

Skin disease diagnosis with deep learning: a review

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