Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.

In this paper, we propose a novel and efficient semisupervised technique for automated prostate cancer localization using multiparametric magnetic resonance imaging (MRI). This method can be used in guiding biopsy, surgery, and therapy. We systematically present a new segmentation technique by developing a multiparametric graph based random walker (RW) algorithm with automated seed initialization to perform prostate cancer segmentation using multiparametric MRI. RW algorithm has proved to be accurate and fast in segmentation applications; however it requires a set of (user provided) seed points in order to perform segmentation. In this study, we first developed a novel RW method, which can be used with multiparametric MR images and then devised alternative methods that can determine seed points in an automated manner using discriminative classifiers such as support vector machines (SVM). Proposed RW method with automated seed initialization is able to produce improved segmentation results by assigning more weights to the images with more discriminative power.We applied the proposed method to a multiparametric dataset obtained from biopsy confirmed prostate cancer patients. Proposed method produces a sensitivity/ specificity rate of 0.76 and 0.86, respectively. Both visual, quantitative as well as statistical results are presented to show the significant performance improvements. Fisher sign test is used to demonstrate the statistical significance of our results by achieving p-values less than 0.05. This method outperforms available RW and SVM based methods by achieving a high specificity rate while not reducing sensitivity.

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Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front windshield. The developed method consists of two stages; first, we localize the driver's face region within the front windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.

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In image classification problems, especially those involving tumor or precancerous lesion, we are usually faced with the situation in which the cost of mistakenly classifying samples in one class is much higher than that of the opposite mistake in the other class. Therefore it is essential to include cost information about classes in our classification methods. This paper applies a cost-sensitive 2ν-SVM classification scheme to cervical cancer images to separate diseased regions from healthy tissue. Using this method, we are able to specify a higher weight to the class that is deemed more important. To the best of our knowledge, cost-sensitive SVM based medical image classification has not been done before. We specifically target segmenting disease regions in digitized uterine cervix images in a NCI/NLM archive of 60,000 images. Our second contribution is the introduction of a multiple classifier scheme instead of the traditional single classifier model. Using the multiple classifier scheme improves significantly classification accuracy as demonstrated by our experiments.

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