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Visual information obtained from endoscopy in laparoscopic surgery plays an important role in surgery navigation and provides a mean for efficient and effective image-based instrument tracking. Instrument tracking based on monocular vision is a well-researched field, in recent years, stereo vision based techniques have attracted the interests of the academic community. In this work, we propose two...
In order to quickly and accurately detect microaneurysms of diabetic retinopathy images, a specific method is proposed in this paper, which eliminates useless information based on overall threshold, computes the set of overall connection domain, and precisely orientates microaneurysms by local threshold segmentation and local connection domain identification. Experimental results testify the good...
Discrimination of benign and malignant mammographic masses based on supervised and unsupervised learning methods help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram. For predicting the outcomes of breast biopsies, we propose Rotation Forest with twelve decision trees algorithms as base classifiers and Principal Component Analysis (PCA) as filter...
Nowadays The research of electrophysiology signal has attracted much attention in clinical discipline. Original experiment record of electrophysiology is significant for further research, especially for the study of Channelopathy. It is difficult to repeat this experiment because of the special cases or the restrictions of the experimental equipment and the cost of experiment. Therefore, it is necessary...
Mammography is a well established imaging technique for showing tissue abnormalities of breast and has been proven to reduce death rate due to breast cancer in screened populations of women. The proposed method classifies the breast tissues according to severeness of abnormality (benign or malign) using combined transform domain features. The discrete wavelet transform (DWT) features are merged with...
In this paper, we demonstrate the effectiveness of using statistical shape priors to recover shape descriptors from occluded objects in a level set based variational framework. Parameters that balance curve evolution forces are estimated systematically through embedded discrete Conditional Random Field (CRF). In addition, our approach exploits the benefit of using spectral data to construct a local...
This paper presents how using a correspondence-based interpolation scheme for 3D image registration improves the registration accuracy. The interpolator takes into account correspondences across slices, which is an advantage, particularly when the volume has thick slices, and where anatomies lie non-parallel to the slice direction. We use our previously presented approach for correspondence-based...
Probabilistic atlases present prior knowledge about the spatial distribution of various structures or tissues in a population, used commonly in segmentation. We propose three methods for generating probabilistic atlases: 1) the atlases are constructed in a template space using dense non-rigid transformations and transformed to the space of unseen data, 2) as the method 1 but atlas selection is performed...
We present the first use of multi-region FDG-PET data for classification of subjects from the Alzheimer's Disease Neuroimaging Initiative. Image data were obtained from 69 healthy controls, 71 AD patients, and 147 patients with a baseline diagnosis of MCI. Anatomical segmentations were automatically generated in the native MRI-space of each subject, and the mean signal intensity per cubic millimetre...
An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which...
The endocardium tracking in ultrasound images is challenging due to large shape variations and the signal dropout. In this paper, we present a method to fuse multiple information sources to robustly track the endocardium. The first novelty of the method is to perform tracking in a straightened shape space, to minimize the image pattern changes caused by cardiac motions. Straightened images are used...
In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation....
The estimation of the mid-sagittal plane (MSP) is a known problem with several applications in neuroimage analysis. As advance to the state-of-the-art, we present a considerably better approach for MSP extraction based on bilateral symmetry maximization and a more suitable error metric to compare MSP estimation methods. The proposed method was quantitatively evaluated using three other state-of-the-art...
We address the problem of segmenting high angular resolution diffusion images of the brain into cerebral regions corresponding to distinct white matter fiber bundles. We cast this problem as a manifold clustering problem in which distinct fiber bundles correspond to different submanifolds of the space of orientation distribution functions (ODFs). Our approach integrates tools from sparse representation...
In this paper, we present a novel methodology for computing statistical shape models (SSM's) by leveraging the medial axis model to determine shape variations between objects. Landmark based SSM's (LSSM's) are a popular approach to describing valid shape variation in an object of interest by applying principal component analysis to a set of landmarks on the surface of the object. However, defining...
Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost,...
Diffusion Tensor Imaging (DTI) has emerged as a reliable, non-invasive method of characterizing tissue micro-structure using MRI, but is limited by long scan time and low SNR. In order to accelerate acquisition, different strategies for undersampling and a model-based reconstruction method are presented. The model-based approach estimates diffusion tensors directly from undersampled k-space data via...
This paper presents a novel unsupervised vascular segmentation algorithm which is applied to retinal fundus images, however could be generalised to any two-dimensional vascular image. The algorithm presents a new fully automatic framework for vessel segmentation and comprises the following features: novel application of the NPWindows method for intensity distribution estimation on localised `image...
In this paper, a novel tree-based registration framework is proposed for achieving fast and accurate registration by providing a more appropriate initial deformation field for the image under registration. Specifically, in the training stage, all training real images and a selected portion of simulated images are organized into a combinative tree with the template as the root, and then each training...
In order to more accurately detect drusen and vessel from retinal fundus images, we proposed a set of new features and presented a learning based detection scheme. These features are designed to describe the variation patterns of image's local geometrical structure across various scales. Theoretical analysis and a series of preliminary experimental results demonstrate the extra ability and high accuracy...
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