In vivo measurement of skin microrelief using photometric stereo in the presence of interreflections.

This paper proposes and describes an implementation of a photometric stereo-based technique for in vivo assessment of three-dimensional (3D) skin topography in the presence of interreflections. The proposed method illuminates skin with red, green, and blue colored lights and uses the resulting variation in surface gradients to mitigate the effects of interreflections. Experiments were carried out on Caucasian, Asian, and African American subjects to demonstrate the accuracy of our method and to validate the measurements produced by our system. Our method produced significant improvement in 3D surface reconstruction for all Caucasian, Asian, and African American skin types. The results also illustrate the differences in recovered skin topography due to the nondiffuse bidirectional reflectance distribution function (BRDF) for each color illumination used, which also concur with the existing multispectral BRDF data available for skin.

Unsupervised sub-segmentation for pigmented skin lesions.

BACKGROUND: Early identification of malignant melanoma with the surgical removal of thin lesions is the most effective treatment for skin cancers. A computer-aided diagnostic system assists to improve the diagnostic accuracy, where segmenting lesion from normal skin is usually considered as the first step. One of the challenges in the automated segmentation of skin lesions arises from the fact that darker areas within the lesion should be considered separate from the more general suspicious lesion as a whole, because these pigmented areas can provide significant additional diagnostic information. METHODS: This paper presents, for the first time, an unsupervised segmentation scheme to allow the isolation of normal skin, pigmented skin lesions, and interesting darker areas inside the lesion simultaneously. An adaptive mean-shift is first applied with a 5D spatial colour-texture feature space to generate a group of homogenous regions. Then the sub-segmentation maps are calculated by integrating maximal similarity-based region merging and the kernel k-means algorithm, where the number of segments is defined by a cluster validity measurement. RESULTS: The proposed method has been validated extensively on both normal digital photographs and dermoscopy images, which demonstrates competitive performance in achieving automatic segmentation. The isolated dark areas have proved helpful in the discrimination of malignant melanomas from atypical benign nevi. Compared with the results obtained from the asymmetry measure of the entire lesion, the asymmetry distribution of the isolated dark areas helped increase the accuracy of the identification of malignant melanoma from 65.38% to 73.07%, and this classification accuracy reached 80.77% on integrating both asymmetry descriptors. CONCLUSION: The proposed segmentation scheme gives the lesion boundary closed to the manual segmentation obtained by experienced dermatologists. The initial classification results indicate that the study of the distributions of darker areas inside the lesions is very promising in characterizing melanomas.

Assessment of cosmesis after breast reconstruction surgery: a systematic review.

BACKGROUND: Breast reconstruction (BR) is undertaken to improve cosmetic outcomes, but how this is optimally assessed is uncertain. This review summarises current methods for assessing cosmesis after reconstructive surgery and makes recommendations for future practice. METHODS: A comprehensive systematic review identified all studies with 20 or more participants that evaluated the cosmetic outcome of BR. Four evaluation criteria (reporting of study inclusion criteria, type and timing of BR and timing of assessment) were used to assess study quality. Articles reporting at least three of the four criteria were considered robust and further summarised to report methods of cosmetic assessment, assessor details and the scoring systems used. RESULTS: 122 primary papers assessed cosmesis in 11,308 women with median follow-up of 28.8 months (range 18.0-42.9 months). Cosmesis was assessed by either healthcare professionals or patients in 33 (27.1%) and 37 studies (30.3%), respectively, and by both professionals and patients in 52 (42.6%). Professional assessments included 43 (40.2%) clinical, 49 (45.8%) photographic and 13 (12.1%) geometric assessments conducted by between 1 and 26 observers. Surgeons were most frequently involved in assessments (n = 71, 67.6%), but in 38 (36.1%) papers the assessor's profession was not reported. Twenty-seven (25.7%) papers used previously published assessment scale. Patients' views were assessed in 89 studies, using questionnaires (n = 63) or interviews (n = 12); 14 (15.7%) did not report how patients' views were obtained. CONCLUSIONS: Current methods for assessing the cosmetic outcome of BR vary widely. A valid patient-centred assessment method is required to fully understand the outcomes of BR and to inform decision-making.

A new method describing border irregularity of pigmented lesions.

BACKGROUND/PURPOSE: Automatic quantitative characterization of border irregularity generating useful descriptors is a highly important task for computer-aided diagnosis of melanoma. This paper proposes a novel approach to describe the border irregularity of melanomas aiming at achieving higher recognition rates. METHODS: By introducing a boundary characteristic description, which we call a centroid distance diagram (CDD), a compact-supported mapping, called the centroid distance curve, can be extracted from this diagram. The centroid distance curve establishes the projection from angular orientations to the sum of the lengths of those line segments connecting the lesion centroid and border points. Border irregularity descriptors generated from CDDs include the non-centroid-convexity index, the maximum-minimum distance indicator, the standard deviation of centroid distance curves and the maximum magnitude of non-zero frequency elements of centroid distance curves after discrete Fourier transforms. Upper limits of the error boundaries involved in these descriptors are estimated. RESULTS: Experimental studies are based on 60 melanoma and 107 benign lesion images collected from local pigmented lesion clinics. By applying the proposed descriptors, receiver operating characteristic (ROC) curves are constructed by projecting the features into a linear space learned from samples. The optimal sensitivity and specificity for the proposed method are 74.2% and 72.6%. The total area enclosed by the corresponding ROC curve is 0.788. In addition, as the training and testing study for melanoma recognition in the literature is largely missing, a comprehensive comparative study is conducted by randomly dividing the data into two groups: one for training and one for testing. For the testing group, the best mean sensitivity obtained with the descriptors proposed in this paper reaches 71.8% and the standard deviation is 10.1%. The specificity for the testing group corresponding to the optimal sensitivity is 69.8%, with a standard deviation of 7.2%. CONCLUSION: This study suggests that in terms of sensitivity, descriptors extracted from CDDs are the most powerful ones in characterizing the border irregularity of melanomas.

Obtaining malignant melanoma indicators through statistical analysis of 3D skin surface disruptions.

BACKGROUND/PURPOSE: It has been observed that disruptions in skin patterns are larger for malignant melanoma (MM) than benign lesions. In order to extend the classification results achieved for 2D skin patterns, this work intends to investigate the feasibility of lesion classification using 3D skin surface texture, in the form of surface normals acquired from a previously built six-light photometric stereo device. MATERIAL AND METHODS: The proposed approach seeks to separate MM from benign lesions through analysis of the degree of surface disruptions in the tilt and slant direction of surface normals, so called skin tilt pattern and skin slant pattern. A 2D Gaussian function is used to simulate a normal region of skin for comparison with a lesion's observed tilt and slant patterns. The differences associated with the two patterns are estimated as the disruptions in the tilt and slant pattern respectively for lesion classification. RESULTS: Preliminary studies on 11 MMs and 28 benign lesions have given Receiver operating characteristic areas of 0.73 and 0.85 for tilt and slant pattern, respectively, which are better than 0.65 previously obtained for the skin line direction using the same samples. CONCLUSIONS: This paper has demonstrated an important application of 3D skin texture for computer-assisted diagnosis of MM in vivo. By taking advantage of the extra dimensional information, preliminary studies suggest that some improvements over the existing 2D skin line pattern approach for the differentiation between MM and benign lesions.

Solitary neurofibroma of the face masquerading as a low-flow vascular malformation - case report and experience of management.

This case report presents a 34-year-old woman who was referred to our regional plastic surgery unit following a 32-year history of a progressively enlarging mass overlying the left maxilla. The mass was initially diagnosed and treated as a low-flow vascular malformation. However, subsequent histopathological assessment confirmed the diagnosis of a cutaneous neurofibroma. To the best of our knowledge, there are only two other reported cases of a solitary neurofibroma arising from the soft tissue of the face, and this is the first reported case in the United Kingdom (UK). This article highlights difficulties in pre-operative diagnosis of solitary facial neurofibromas. We present our experience in managing this unusual case, discuss radiological clues to aid diagnosis and provide a review of the literature.

Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas.

Computerised analysis on skin lesion images has been reported to be helpful in achieving objective and reproducible diagnosis of melanoma. In particular, asymmetry in shape, colour and structure reflects the irregular growth of melanin under the skin and is of great importance for diagnosing the malignancy of skin lesions. This paper proposes a novel asymmetry analysis based on a newly developed pigmentation elevation model and the global point signatures (GPSs). Specifically, the pigmentation elevation model was first constructed by computer-based analysis of dermoscopy images, for the identification of melanin and haemoglobin. Asymmetry of skin lesions was then assessed through quantifying distributions of the pigmentation elevation model using the GPSs, derived from a Laplace-Beltrami operator. This new approach allows quantifying the shape and pigmentation distributions of cutaneous lesions simultaneously. Algorithm performance was tested on 351 dermoscopy images, including 88 malignant melanomas and 263 benign naevi, employing a support vector machine (SVM) with tenfold cross-validation strategy. Competitive diagnostic results were achieved using the proposed asymmetry descriptor only, presenting 86.36 % sensitivity, 82.13 % specificity and overall 83.43 % accuracy, respectively. In addition, the proposed GPS-based asymmetry analysis enables working on dermoscopy images from different databases and is approved to be inherently robust to the external imaging variations. These advantages suggested that the proposed method has good potential for follow-up treatment.

Enhanced 3D curvature pattern and melanoma diagnosis.

This article describes an enhanced curvature pattern based melanoma diagnosis system using convolution techniques and ensemble classifiers. We extract the 3D data of melanoma with a photometric stereo device first. Then differential forms of the melanoma surface can be extracted with the convolution method proposed. After extracting 3D based differential forms, statistical moments of enhanced principal curvatures of skin surfaces are calculated to describe the geometrical texture patterns. Finally, ensemble classifiers are constructed whose optimal mean sensitivity and specificity can reach 89.24 percent and 87.62 percent respectively. Comparisons with skin tilt/slant pattern based 3D shape characterization method and 2D methods like color variation and border irregularity are also included.

Biological indexes based reflectional asymmetry for classifying cutaneous lesions.

This paper proposes a novel reflectional asymmetry descriptor to quantize the asymmetry of the cutaneous lesions for the discrimination of malignant melanoma from benign nevi. A pigmentation elevation model of the biological indexes is first constructed, and then the asymmetry descriptor is computed by minimizing the histogram difference of the global point signatures of the pigmentation model. Melanin and Erythema Indexes are used instead of the original intensities in colour space to characterize the pigmentation distribution of the cutaneous lesions. 311 dermoscopy images are used to validate the algorithm performance, where 88.50% sensitivity and 81.92% specificity have been achieved when employing an SVM classifier.