Pattern analysis of dermoscopic images based on Markov random fields

Serrano, Carmen; Acha, Begoña

doi:10.1016/j.patcog.2008.07.011

Cited by 72 publications

(38 citation statements)

References 26 publications

(29 reference statements)

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“…As a fundamental step towards computer-aided diagnosis of skin cancers, automatic detection and analysis of local dermoscopic structures [16]- [23] and global patterns [24]- [28] have been frequently addressed in the literature. However, even though the presence of Irregular streaks is highly suggestive for malignancy of a lesion, the modeling, detection, and analysis of streak lines and starburst pattern have very rarely been used for automated skin lesion diagnosis.…”

Section: Previous Workmentioning

confidence: 99%

Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Sadeghi

Lee

McLean

et al. 2013

IEEE Trans. Med. Imaging

112

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Abstract-Irregular streaks are important clues for Melanoma (a potentially fatal form of skin cancer) diagnosis using dermoscopy images. This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions, by further analyzing the appearance of detected streak lines, and performing a three-way classi cation for streaks, Absent, Regular, and Irregular, in a pigmented skin lesion. In addition, the directional pattern of detected lines is analyzed to extract their orientation features in order to detect the underlying pattern. The method uses a graphical representation to model the geometric pattern of valid streaks and the distribution and coverage of the structure. Using these proposed features of the valid streaks along with the color and texture features of the entire lesion, an accuracy of 76.1% and weighted average area under ROC curve (AUC) of 85% is achieved for classifying dermoscopy images into streaks Absent, Regular, or Irregular on 945 images compiled from atlases and the internet without any exclusion criteria. This challenging dataset is the largest validation dataset for streaks detection and classi cation published to date. The data set has also been applied to the two-class sub-problems of Absent/Present classi cation (accuracy of 78.3% with AUC of 83.2%) and to Regular/Irregular classi cation (accuracy 83.6% with AUC of 88.9%). When the method was tested on a cleaned subset of 300 images randomly selected from the 945 images, the AUC increased to 91.8%, 93.2% and 90.9% for the Absent/Regular/Irregular, Absent/Present, and Regular/Irregular problems, respectively.

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Section: Previous Workmentioning

confidence: 99%

Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Sadeghi

Lee

McLean

et al. 2013

IEEE Trans. Med. Imaging

112

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“…3 Serrano et. al extended Markov random field (MRF) to classify dermoscopic patterns of color skin images.…”

Section: Previous Workmentioning

confidence: 99%

Global pattern analysis and classification of dermoscopic images using textons

et al. 2012

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Detecting and classifying global dermoscopic patterns are crucial steps for detecting melanocytic lesions from non-melanocytic ones. An important stage of melanoma diagnosis uses pattern analysis methods such as 7-point check list, Menzies method etc. In this paper, we present a novel approach to investigate texture analysis and classification of 5 classes of global lesion patterns (reticular, globular, cobblestone, homogeneous, and parallel pattern) in dermoscopic images. Our statistical approach models the texture by the joint probability distribution of filter responses using a comprehensive set of the state of the art filter banks. This distribution is represented by the frequency histogram of filter response cluster centers called textons. We have also examined other two methods: Joint Distribution of Intensities (JDI) and Convolutional Restricted Boltzmann Machine (CRBM) to learn the pattern specific features to be used for textons. The classification performance is compared over the Leung and Malik filters (LM), Root Filter Set (RFS), Maximum Response Filters (MR8), Schmid, Laws and our proposed filter set as well as CRBM and JDI. We analyzed 375 images of the 5 classes of the patterns. Our experiments show that the joint distribution of color (JDC) in the L*a*b* color space outperforms the other color spaces with a correct classification rate of 86.8%.

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“…That is, Asymmetry of the lesion's boundaries, Boundary irregularity, Color distributions and lesion Diameters. Alternative classifications based on color texture appearance has been researched recently [2,3], and by employing a biopsy and electron tomography, a classification from 3D cell images has been reported [4]. The technique of dermoscopy used polarized light or oil coverage to make more of a lesion's structure visible [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

Staunton

2013

Pattern Recognition

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The boundary irregularity of skin lesions is of clinical significance for the early detection of malignant melanomas and to distinguish them from other lesions such as benign moles. The structural components of the contour are of particular importance. To extract the structure from the contour, wavelet decomposition was used as these components tend to locate in the lower frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to give position and frequency resolution invariance. Energy distributions among different wavelet sub-bands were then analyzed to extract those with significant levels and differences to enable maximum discrimination.Based on the coefficients in the significant sub-bands, structural components from the original contours were modeled, and a set of statistical and geometric irregularity descriptors researched that were applied at each of the significant sub-bands. The effectiveness of the descriptors was measured using the Hausdorff distance between sets of data from melanoma and mole contours.The best descriptor outputs were input to a back projection neural network to construct a combined classifier system. Experimental results showed that thirteen features from four sub-bands produced the best discrimination between sets of melanomas and moles, and that a small training set of nine melanomas and nine moles was optimum.

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Pattern analysis of dermoscopic images based on Markov random fields

Cited by 72 publications

References 26 publications

Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Global pattern analysis and classification of dermoscopic images using textons

Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

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