Segmentation of skin cancer images

Xu, Lang; Jackowski, Marcel P.; Goshtasby, A. Ardeshir; Roseman, David L.; Bines, Steven; Yu, Clement; Dhawan, Akshaya; Huntley, Arthur C.

doi:10.1016/s0262-8856(98)00091-2

Cited by 185 publications

(86 citation statements)

References 19 publications

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“…The most common and widely used color spaces are the CIE L*a*b* and CIE L*u*v*, described in [9]. These transformations have been shown to produce good segmentation results for some dermatological diagnoses [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Independent Histogram Pursuit for Segmentation of Skin Lesions

Gómez

Butakoff

Ersbøll

et al. 2008

IEEE Trans. Biomed. Eng.

118

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In this paper, an unsupervised algorithm, called the Independent Histogram Pursuit (IHP), for segmenting dermatological lesions is proposed. The algorithm estimates a set of linear combinations of image bands that enhance different structures embedded in the image. In particular, the first estimated combination enhances the contrast of the lesion to facilitate its segmentation. Given an N-band image, this first combination corresponds to a line in N dimensions, such that the separation between the two main modes of the histogram obtained by projecting the pixels onto this line, is maximized. The remaining combinations are estimated in a similar way under the constraint of being orthogonal to those already computed. The performance of the algorithm is tested on five different dermatological datasets. The results obtained on these datasets indicate the robustness of the algorithm and its suitability to deal with different types of dermatological lesions. The boundary detection precision using k-means segmentation was close to 97%. The proposed algorithm can be easily combined with the majority of classification algorithms.

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Section: Introductionmentioning

confidence: 99%

Independent Histogram Pursuit for Segmentation of Skin Lesions

Gómez

Butakoff

Ersbøll

et al. 2008

IEEE Trans. Biomed. Eng.

118

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“…4 It is hard to directly compare our approach to other algorithms because most other algorithms are designed for 1) other input modalities (e.g., dermoscopy) and 2) only melanocytic lesions. We did evaluate the Skin Cancer Segmentation software package [2], which is based on colour differences between the lesion and the surrounding skin and the thresholding algorithm (here called the DT method). Of our 20 test images, 12 failed totally, because the lesions did not have significant pigmentation.…”

Section: Resultsmentioning

confidence: 99%

“…Upon comparison, we conclude that both depth and texture properties help to improve the segmentation result by reducing error rate from 15.42%±8.67% to 9.68%±5.90%. Our method integrating colour, depth and texture information produces the best results compared to those by the MS method [10], the DT method [2] and dermatologists. There are several potential improvements and follow-up work to be considered: 1) In addition to the structure tensor, other texture features should be considered.…”

Section: Conclusion and Further Workmentioning

confidence: 92%

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Depth Data Improves Skin Lesion Segmentation

Aldridge

Ballerini

et al. 2009

Lecture Notes in Computer Science

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This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A regionbased active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.

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“…Active contours [38] and snake’s method [39] are less attractive due to the complex shape of the melanoma suspicious lesions. Since thresholding does not present any of these disadvantages [40], it could be used in order to get the reliable results.…”

Section: Methodsmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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Segmentation of skin cancer images

Cited by 185 publications

References 19 publications

Independent Histogram Pursuit for Segmentation of Skin Lesions

Independent Histogram Pursuit for Segmentation of Skin Lesions

Depth Data Improves Skin Lesion Segmentation

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

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