Pixel-based skin segmentation in psoriasis images.

In this paper, we present a detailed comparison study of skin segmentation methods for psoriasis images. Different techniques are modified and then applied to a set of psoriasis images acquired from the Royal Melbourne Hospital, Melbourne, Australia, with aim of finding the best technique suited for application to psoriasis images. We investigate the effect of different colour transformations on skin detection performance. In this respect, explicit skin thresholding is evaluated with three different decision boundaries (CbCr, HS and rgHSV). Histogram-based Bayesian classifier is applied to extract skin probability maps (SPMs) for different colour channels. This is then followed by using different approaches to find a binary skin map (SM) image from the SPMs. The approaches used include binary decision tree (DT) and Otsu's thresholding. Finally, a set of morphological operations are implemented to refine the resulted SM image. The paper provides detailed analysis and comparison of the performance of the Bayesian classifier in five different colour spaces (YCbCr, HSV, RGB, XYZ and CIELab). The results show that histogram-based Bayesian classifier is more effective than explicit thresholding, when applied to psoriasis images. It is also found that decision boundary CbCr outperforms HS and rgHSV. Another finding is that the SPMs of Cb, Cr, H and B-CIELab colour bands yield the best SMs for psoriasis images. In this study, we used a set of 100 psoriasis images for training and testing the presented methods. True Positive (TP) and True Negative (TN) are used as statistical evaluation measures.

A deep bag-of-features model for the classification of melanomas in dermoscopy images.

Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level representations using machine learning techniques. Such interest has been growing steadily in the field of medical image diagnosis, particularly in melanoma classification. In this paper, a novel application of deep learning (stacked sparse auto-encoders) is presented for skin lesion classification task. The stacked sparse auto-encoder discovers latent information features in input images (pixel intensities). These high-level features are subsequently fed into a classifier for classifying dermoscopy images. In addition, we proposed a new deep neural network architecture based on bag-of-features (BoF) model, which learns high-level image representation and maps images into BoF space. Then, we examine how using this deep representation of BoF, compared with pixel intensities of images, can improve the classification accuracy. The proposed method is evaluated on a test set of 244 skin images. To test the performance of the proposed method, the area under the receiver operating characteristics curve (AUC) is utilized. The proposed method is found to achieve 95% accuracy.

Automated colour identification in melanocytic lesions.

Colour information plays an important role in classifying skin lesion. However, colour identification by dermatologists can be very subjective, leading to cases of misdiagnosis. Therefore, a computer-assisted system for quantitative colour identification is highly desirable for dermatologists to use. Although numerous colour detection systems have been developed, few studies have focused on imitating the human visual perception of colours in melanoma application. In this paper we propose a new methodology based on QuadTree decomposition technique for automatic colour identification in dermoscopy images. Our approach mimics the human perception of lesion colours. The proposed method is trained on a set of 47 images from NIH dataset and applied to a test set of 190 skin lesions obtained from PH2 dataset. The results of our proposed method are compared with a recently reported colour identification method using the same dataset. The effectiveness of our method in detecting colours in dermoscopy images is vindicated by obtaining approximately 93% accuracy when the CIELab1 colour space is used.

Incidence and risk factors associated with the patency of ductus arteriosus in preterm infants with respiratory distress syndrome in Kuwait.

OBJECTIVE: Patent ductus arteriosus (PDA) is considered to be an important cause of morbidity and mortality among preterm infants. The aim of this study is to determine the incidence of PDA in ventilated preterm infants with respiratory distress syndrome (RDS) and to evaluate the role of some antenatal risk factors on its occurrence in our population. METHODS: The case records of the preterm infants of <34 weeks gestational age, who were ventilated for RDS at the neonatal intensive care unit of Maternity Hospital, Safat, Kuwait, between March 1998 and February 1999, were reviewed. Diagnosis of PDA was based on echocardiographic findings. The association between the risk factors chosen and the PDA was also evaluated. RESULTS: A total of 101 infants whose gestational ages ranged between 25-33 weeks, and birth weights between 685-1580 grams were included. Fifty-four had a significant PDA (53.4%). Maternal diabetes and antepartum hemorrhage (APH), birth weights, gestational ages, multiplicity and gender of the infants were found to be related to the incidence of PDA. CONCLUSION: The incidence of PDA in our ventilated preterm infants with RDS is similar to those reported from other neonatal units outside Kuwait. There are some factors that may identify babies, who are prone to develop PDA, which need to be confirmed by further prospective studies using a larger population.