Ning Situ

A narrow band graph partitioning method for skin lesion segmentation

Accurate skin lesion segmentation is critical for automated early skin cancer detection and diagnosis. In this paper, we present a novel multi-modal skin lesion segmentation method based on region fusion and narrow band energy graph partitioning. The proposed method can handle challenging characteristics of skin lesions, such as topological changes, weak or false edges, and asymmetry. Extensive testing demonstrated that in this method complex contours are detected correctly while topological changes of evolving curves are managed naturally. The accuracy of the method was quantified using a lesion similarity measure and lesion segmentation error ratio, Our results were validated using a large set of epiluminescence microscopy (ELM) images acquired using cross-polarization ELM and side-transillumination ELM. Our findings demonstrate that the new method can achieve improved robustness and better overall performance compared to other state-of-the-art segmentation methods.

Functional connectivity networks in the autistic and healthy brain assessed using Granger causality

In this study, we analyze brain connectivity based on Granger causality computed from magnetoencephalographic (MEG) activity obtained at the resting state in eight autistic and eight normal subjects along with measures of network connectivity derived from graph theory in an attempt to understand how communication in a human brain network is affected by autism. A connectivity matrix was computed for each subject individually and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fishers criterion to rank the features and identify the best subset for maximum separation of the groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide 87.5% accuracy in separating the two groups. These findings suggest that analysis of functional connectivity patterns may provide a valuable method for the early detection of autism.

Implementation of the 7-point checklist for melanoma detection on smart handheld devices

In this paper we implement the 7-point checklist, a set of dermoscopic criteria widely used by clinicians for melanoma detection, on smart handheld devices, such as the Apple iPhone and iPad. The application developed is using sophisticated image processing and pattern recognition algorithms, yet it is light enough to run on a handheld device with limited memory and computational speed. When combined with a commercially available handheld dermoscope that provides proper lesion illumination, this application provides a truly self-contained handheld system for melanoma detection. Such a device can be used in a clinical setting for routine skin screening, or as an assistive diagnostic device in underserved areas and in developing countries with limited healthcare infrastructure.

Malignant melanoma detection by Bag-of-Features classification

In this paper, we apply a Bag-of-Features approach to malignant melanoma detection based on epiluminescence microscopy imaging. Each skin lesion is represented by a histogram of codewords or clusters identified from a training data set. Classification results using Naive Bayes classification and Support Vector Machines are reported. The best performance obtained is 82.21% on a dataset of 100 skin lesion images. Furthermore, since in melanoma screening false negative errors have a much higher impact and associated cost than false positive ones, we use the Neyman-Pearson score in our model selection scheme.

SkinScan © : A portable library for melanoma detection on handheld devices

We have developed a portable library for automated detection of melanoma termed SkinScan© that can be used on smartphones and other handheld devices. Compared to desktop computers, embedded processors have limited processing speed, memory, and power, but they have the advantage of portability and low cost. In this study we explored the feasibility of running a sophisticated application for automated skin cancer detection on an Apple iPhone 4. Our results demonstrate that the proposed library with the advanced image processing and analysis algorithms has excellent performance on handheld and desktop computers. Therefore, deployment of smartphones as screening devices for skin cancer and other skin diseases can have a significant impact on health care delivery in underserved and remote areas.

Automatic Segmentation of Skin Lesion Images using Evolutionary Strategy

Malignant melanoma has a good prognosis if treated early. Accurate skin lesion segmentation from the background skin is important not only because the shape feature can be directly derived from the process, but also because it can provide a scope for texture analysis. In this paper, we propose an evolutionary strategy based segmentation algorithm to identify the lesion area by an ellipse. It can detect the lesion automatically without setting parameters manually. The method is validated by experiments and comparisons with manually segmentation by an expert and algorithms developed by M. Doshi, et al. (2004).

Functional connectivity changes in mild traumatic brain injury assessed using magnetoencephalography

In this study, we analyzed brain connectivity profiles from 10 mild traumatic brain injury (mTBI) patients and 10 age- and gender-matched normal controls. We computed Granger causality measures from magnetoencephalographic (MEG) activity obtained at the resting state, in an attempt to understand how the default network is affected by mTBI. A connectivity matrix was computed for each subject individually and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fishers criterion to rank the features and identify the best subset for maximum separation of the groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide a minimum of 85% classification accuracy in separating the two groups, with a sensitivity and specificity of 90% and 80%, respectively. These findings suggest that analysis of functional connectivity patterns may provide a valuable tool for early detection of mTBI.

Automatic segmentation of skin lesion images using evolution strategies

Abstract Skin cancer is the most common form of cancer and represents 50 % of all new cancers detected each year. However, if detected at an early stage, simple and economic treatments can cure most skin cancers. Accurate skin lesion segmentation is critical in automated skin cancer early detection and diagnosis systems. In this paper, we present an evolution strategy (ES) based segmentation algorithm to identify the lesion area within an ellipse. The method is applied to a set of 51 crosspolarization and 60 transillumination images segmented manually by a dermatologist, which are used as ground truth. Unlike most segmentation procedures, the proposed ES-based method can detect the lesion automatically without setting parameters and initial values by trial and error. Compared to results from our previous work, the ES-based method gives comparable accuracy for easily segmented images and much better results for images with either higher noise level, less prominent edges, or very small size lesions.

Evaluating sampling strategies of dermoscopic interest points

Among the most critical components of a computerized system for automated melanoma detection is image sampling and pooling of the extracted features. In this paper, we propose a new method for sampling and pooling based on a combination of spatial pooling and graph theory features. The performance of the new method is evaluated using a dataset of more than 1,500 images representing pigmented skin lesions of known pathology. In our comparisons, we include several methods ranging from simple and multi-scale sampling on a regular grid to more sophisticated approaches, such as blob and curvilinear structure detectors. Our results show that, despite its simplicity, simple sampling on a regular grid provides highly competitive performance, compared to the more sophisticated approaches, while multi-scale sampling yields only trivial improvements. However, the proposed method provides significant performance improvement in terms of sensitivity and area under the receiver operating characteristic curve (95% t-test), and the best performance in terms of specificity compared to all other methods explored.

Modeling spatial relation in skin lesion images by the graph walk kernel

Early skin cancer detection with the help of dermoscopic images is becoming more and more important. Previous methods generally ignored the spatial relation of the pixels or regions inside the lesion. We propose to employ a graph representation of the skin lesion to model the spatial relation. We then use the graph walk kernel, a similarity measure between two graphs, to build a classifier based on support vector machines for melanoma detection. In experiments, we compare the sensitivities and specificities of models with and without spatial information. Experimental results show that the model with spatial information performs the best in both sensitivity and specificity. Statistical test indicates that the improvement is significant.