Apparent Ultra-High b-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields.

A promising, recently explored, alternative to ultra-high b-value diffusion weighted imaging (UHB-DWI) is apparent ultra-high b-value diffusion-weighted image reconstruction (AUHB-DWR), where a computational model is used to assist in the reconstruction of apparent DW images at ultra-high b -values. Firstly, we present a novel approach to AUHB-DWR that aims to improve image quality. We formulate the reconstruction of an apparent DW image as a hidden conditional random field (HCRF) in which tissue model diffusion parameters act as hidden states in this random field. The second contribution of this paper is a new generation of fully connected conditional random fields, called the hidden stochastically fully connected conditional random fields (HSFCRF) that allows for efficient inference with significantly reduced computational complexity via stochastic clique structures. The proposed AUHB-DWR algorithms, HCRF and HSFCRF, are evaluated quantitatively in nine different patient cases using Fisher's criteria, probability of error, and coefficient of variation metrics to validate its effectiveness for the purpose of improving intensity delineation between expert identified suspected cancerous and healthy tissue within the prostate gland. The proposed methods are also examined using a prostate phantom, where the apparent ultra-high b-value DW images reconstructed using the tested AUHB-DWR methods are compared with real captured UHB-DWI. The results illustrate that the proposed AUHB-DWR methods has improved reconstruction quality and improved intensity delineation compared with existing AUHB-DWR approaches.

Physiological characterization of skin lesion using non-linear random forest regression model.

The current diagnostic technique for melanoma solely relies on the surface level of skin and under-skin information is neglected. Since physiological features of skin such as melanin are closely related to development of melanoma, the non-linear physiological feature extraction model based on random forest regression is proposed. The proposed model characterizes the concentration of eumelanin and pheomelanin from standard camera images or dermoscopic images, which are conventionally used for diagnosis of melanoma. For the validation, the phantom study and the separability test using clinical images were conducted and compared against the state-of-the art non-linear and linear feature extraction models. The results showed that the proposed model outperformed other comparing models in phantom and clinical experiments. Promising results show that the quantitative characterization of skin features, which is provided by the proposed method, can allow dermatologists and clinicians to make a more accurate and improved diagnosis of melanoma.

Enhanced classification of malignant melanoma lesions via the integration of physiological features from dermatological photographs.

Traditional methods for early detection of melanoma rely upon a dermatologist to visually assess a skin lesion using the ABCDE (Asymmetry, Border irregularity, Color variegation, Diameter, Evolution) criteria before confirmation can be done through biopsy by a pathologist. However, this visual assessment strategy taken by dermatologists is hampered by clinician subjectivity and suffers from low sensitivity. Computer-aided diagnostic methods based on dermatological photographs are being developed to aid in the melanoma diagnosis process, but most of these methods rely only on superficial, topographic features that can be limiting in characterizing melanoma. In this work, a hybrid feature model is introduced for characterizing skin lesions that combines low-level and high-level features, and augments them with a set of physiological features extracted from dermatological photographs using a nearest-neighbor nonlinear model to improve classification performance. The physiological features extracted from the lesion for the proposed hybrid feature model include those based on: i) eumelanin concentrations, ii) pheomelanin concentrations, and iii) blood oxygen saturation. The proposed hybrid feature model was evaluated on 206 dermatological photographs of skin lesions (119 confirmed melanoma cases, 87 confirmed non-melanoma cases) using a cross validation scheme. The experimental results show that the proposed hybrid feature model, with integrated physiological features, provided improved sensitivity, specificity, precision and accuracy for the purpose of melanoma classification.