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Accurate skin lesion segmentation is an important yet challenging problem for medical image analysis. The skin lesion segmentation is subject to variety of challenges such as the significant pattern and colour diversity found within the lesions, presence of various artifacts, etc. In this paper, we present two fully convolutional networks with several side outputs to take advantage of discriminative...
A review of recent works by our group in the segmentation and quantification of oncological lesions in 18-fluoro-deoxy-glucose (FDG) positron emission tomography (PET) images is given, stressing the underlying model assumption. In a first approach, a targeted reconstruction strategy was set in the framework of linear space variant (LSV) reconstruction from projections. Resolution recovery by ordered...
Fluid Attenuated Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation...
A weakly supervised image classification framework is presented in this paper. Given reference images marked by clinicians as relevant or irrelevant, we learn to automatically detect relevant patterns, i.e. patterns that only appear in relevant images. After training, relevant patterns are sought in unseen images in order to classify each image as relevant or irrelevant. No manual segmentations are...
Mammography is the most effective procedure for the early detection of breast cancer. In this paper, we develop a novel algorithm to detect suspicious lesions in mammograms. The algorithm utilizes the combination of adaptive global thresholding segmentation and adaptive local thresholding segmentation on a multiresolution representation of the original mammogram. The algorithm has been verified with...
The location, size and shape of Multiple Sclerosis (MS) lesions are often used to diagnose and track disease progression. In order to effectively compare lesions in MRI stacks for the same patient imaged at intervals, these stacks must be aligned. This automatic alignment method was designed to minimize modification of segmented pixel values. The aligned lesion stacks can be browsed independently...
Automated segmentation of pigmented skin lesions (PSLs) from dermoscopy images is an important step for computer-aided diagnosis of skin cancer. The segmentation task involves classifying each image pixel as either lesion or skin. It is challenging because lesion and skin can often have similar appearance. We present a novel exemplar-based algorithm for lesion segmentation which leverages the context...
The magnetic resonance contrast of a neuroimaging data set has strong impact on the utility of the data in image analysis tasks, such as registration and segmentation. Lengthy acquisition times often prevent routine acquisition of multiple MR contrast images, and opportunities for detailed analysis using these data would seem to be irrevocably lost. This paper describes an example based approach which...
Diabetic retinopathy is a major cause of blindness. Earliest signs of diabetic retinopathy are damage to blood vessels in the eye and then the formation of lesions in the retina. This paper presents an automated method for the detection of bright lesions (exudates) in retinal images. In this work, an adaptive thresholding based on a novel algorithm for pure splitting of the image is proposed. A coarse...
This work is directed at reducing patient induced blurring in SPECT imaging due to breathing motion. As image resolution improves this breathing motion is becoming increasingly significant. Method: The NCAT phantom and an associated medical image processing package (RMDP) are used to obtain a breathing cycle of images (both CT and corresponding SPECT) and a full organ segmentation. A process termed...
We have implemented a scheme for simulating realistic dynamic PET data from real MR acquisitions. This toolkit uses a series of MR acquisitions, image registrations and segmentations. PET images are simulated assigning typical values to the segmented images, and manually inserting additional lesions. The data are simulated using analytic forward-projections (including attenuation) with STIR, providing...
Various studies have been conducted to expand the utilization of combined positron emission tomography and computed tomography (PET/CT) covering cases of infection and inflammation. PET images provide the functional activity of a lesion while CT images demonstrate the anatomical location. Hence, existence of infected lesions can be recognized in PET image but since the structural position can not...
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