Accurate HEp-2 cell classification based on sparse bag of words coding.

Autoimmune diseases (AD) are the abnormal response of the immune system of the body to healthy tissues. ADs have generally been on the increase. Efficient computer aided diagnosis of ADs through classification of the human epithelial type 2 (HEp-2) cells become beneficial. These methods make lower diagnosis costs, faster response and better diagnosis repeatability. In this paper, we present an automated HEp-2 cell image classification technique that exploits the sparse coding of the visual features together with the Bag of Words model (SBoW). In particular, SURF (Speeded Up Robust Features) and SIFT (Scale-invariant feature transform) features are specially integrated to work in a complementary fashion. This method helps greatly improve the cell classification accuracy. Additionally, a hierarchical max-pooling method is proposed to aggregate the local sparse codes in different layers to provide final feature vector. Furthermore, various parameters of the dictionary learning including the dictionary size, the learning iteration number, and the pooling strategy is also investigated. Experiments conducted on publicly available datasets show that the proposed technique clearly outperforms state-of-the-art techniques in cell and specimen levels.

A computationally efficient method for reconstructing sequences of MR images from undersampled k-space data.

In this paper, we propose a Compressive Sensing based approach to the problem of real-time reconstruction of MR image sequences. Our proposed method is able to extract useful priori information and incorporate it into a modified iterative thresholding algorithm for fast casual reconstruction of MR images from highly undersampled k-space data. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, while having a lower computational complexity and memory requirements compared to the other state-of-the-art methods.

On ECG reconstruction using weighted-compressive sensing.

The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices.

3D reconstruction of neurons in electron microscopy images.

With the prevalence of brain-related diseases like Alzheimer in an increasing ageing population, Connectomics, the study of connections between neurons of the human brain, has emerged as a novel and challenging research topic. Accurate and fully automatic algorithms are needed to deal with the increasing amount of data from the brain images. This paper presents an automatic 3D neuron reconstruction technique where neurons within each slice image are first segmented and then linked across multiple slices within the publicly available Electron Microscopy dataset (SNEMI3D). First, random Forest classifier is adapted on top of superpixels for the neuron segmentation within each slice image. The maximum overlap between two consecutive images is then calculated for neuron linking, where the adjacency matrix of two different labeling of the segments is used to distinguish neuron merging and splitting. Experiments over the SNEMI3D dataset show that the proposed technique is efficient and accurate.

High-order local spatial context modeling by spatialized random forest.

In this paper, we propose a novel method for spatial context modeling toward boosting visual discriminating power. We are particularly interested in how to model high-order local spatial contexts instead of the intensively studied second-order spatial contexts, i.e., co-occurrence relations. Motivated by the recent success of random forest in learning discriminative visual codebook, we present a spatialized random forest (SRF) approach, which can encode an unlimited length of high-order local spatial contexts. By spatially random neighbor selection and random histogram-bin partition during the tree construction, the SRF can explore much more complicated and informative local spatial patterns in a randomized manner. Owing to the discriminative capability test for the random partition in each tree node's split process, a set of informative high-order local spatial patterns are derived, and new images are then encoded by counting the occurrences of such discriminative local spatial patterns. Extensive comparison experiments on face recognition and object/scene classification clearly demonstrate the superiority of the proposed spatial context modeling method over other state-of-the-art approaches for this purpose.

Active visual segmentation.

Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the "optimal" closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the "optimal" closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.

Hierarchical segmentation-based image coding using hybrid quad-binary trees.

A novel segmentation-based image approximation and coding technique is proposed. A hybrid quad-binary (QB) tree structure is utilized to efficiently model and code geometrical information within images. Compared to other tree-based representation such as wedgelets, the proposed QB-tree based method is more efficient for a wide range of contour features such as junctions, corners and ridges, especially at low bit rates.

Modeling interaction for segmentation of neighboring structures.

This paper presents a new method for segmenting medical images by modeling interaction between neighboring structures. Compared to previously reported methods, the proposed approach enables simultaneous segmentation of multiple neighboring structures for improved robustness. During the segmentation process, the object contour evolution and shape prior estimates are influenced by the interactions between neighboring shapes consisting of attraction, repulsion, and competition. Instead of estimating the a priori shape of each structure independently, an interactive maximum a posteriori shape estimation method is used for estimating the shape priors by considering shape prior distribution, neighboring shapes, and image features. Energy functionals are then formulated to model the interaction and segmentation. With the proposed method, neighboring structures with similar intensities and/or textures, and blurred boundaries can be extracted simultaneously. Experimental results obtained on both synthetic data and medical images demonstrate that the introduced interaction between neighboring structures improves segmentation performance compared with other existing approaches.

A DATA-DRIVEN APPROACH TO PRIOR EXTRACTION FOR SEGMENTATION OF LEFT VENTRICLE IN CARDIAC MR IMAGES.

In this paper, we propose a data-driven approach that extracts prior information for segmentation of the left ventricle in cardiac MR images of transplanted rat hearts. In our approach, probabilistic priors are generated from prominent features, i.e., corner points and scale-invariant edges, for both endo-and epi-cardium segmentation. We adopt a level set formulation that integrates probabilistic priors with intensity, texture, and edge information for segmentation. Our experimental results show that with minimal user input, representative priors are correctly extracted from the data itself, and the proposed method is effective and robust for segmentation of the left ventricle myocardium even in images with very low contrast. More importantly, it avoids inter- and intra- observer variations and makes accurate quantitative analysis of low-quality cardiac MR images possible.

Signal and image approximation using interval wavelet transform.

In signal approximation, classical wavelet synthesis are known to produce Gibbs-like phenomenon around discontinuities when wavelet coefficients in the cone of influence of the discontinuities are quantized. By analyzing a function in a piecewise manner, filtering across discontinuities can be avoided. Using this principle, the interval wavelet transform can generate sparser representations in the vicinity of discontinuities than classical wavelet transforms. This work introduces two new constructions of interval wavelets and shows how they can be used for image compression and upscaling.

Medical image segmentation using minimal path deformable models with implicit shape priors.

This paper presents a new method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the short-comings of existing deformable models and has been successfully applied to segmenting various medical images.

Segmentation of volumetric MRA images by using capillary active contour.

Precise segmentation of three-dimensional (3D) magnetic resonance angiography (MRA) images can be a very useful computer aided diagnosis (CAD) tool for clinical routines. Level sets based evolution schemes, which have been shown to be effective and easy to implement for many segmentation applications, are being applied to MRA data sets. In this paper, we present a segmentation scheme for accurately extracting vasculature from MRA images. Our proposed algorithm models capillary action and derives a capillary active contour for segmentation of thin vessels. The algorithm is implemented using the level set method and has been applied successfully on real 3D MRA images. Compared with other state-of-the-art MRA segmentation algorithms, experiments show that our method facilitates more accurate segmentation of thin blood vessels.

Analysis of esthetic smiles by using computer vision techniques.

Properly aligned teeth and a beautiful smile are the twin goals of orthodontic treatment. Unfortunately, a change in the smile arc is sometimes an unintended consequence of proper alignment. We used 3-dimensional dental models and visualization techniques, including curve-fitting and image-processing algorithms, to analyze smile arcs with respect to different parameters. The results show that smile consonance depends greatly on the conversational distance and the angle of elevation between the viewer and the smile.

Motion compensated lossy-to-lossless compression of 4-D medical images using integer wavelet transforms.

This paper proposes a method for progressive lossy-to-lossless compression of four-dimensional (4-D) medical images (sequences of volumetric images over time) by using a combination of three-dimensional (3-D) integer wavelet transform (IWT) and 3-D motion compensation. A 3-D extension of the set-partitioning in hierarchical trees (SPIHT) algorithm is employed for coding the wavelet coefficients. To effectively exploit the redundancy between consecutive 3-D images, the concepts of key and residual frames from video coding is used. A fast 3-D cube matching algorithm is employed to do motion estimation. The key and the residual volumes are then coded using 3-D IWT and the modified 3-D SPIHT. The experimental results presented in this paper show that our proposed compression scheme achieves better lossy and lossless compression performance on 4-D medical images when compared with JPEG-2000 and volumetric compression based on 3-D SPIHT.

MRA image segmentation with capillary active contour.

Precise segmentation of three-dimensional (3D) magnetic resonance angiography (MRA) image can be a very useful computer aided diagnosis (CAD) tool in clinical routines. Our objective is to develop a specific segmentation scheme for accurately extracting vasculature from MRA images. Our proposed algorithm, called the capillary active contour (CAC), models capillary action where liquid can climb along the boundaries of thin tubes. The CAC, which is implemented based on level sets, is able to segment thin vessels and has been applied for verification on synthetic volumetric images and real 3D MRA images. Compared with other state-of-the-art MRA segmentation algorithms, our experiments show that the introduced capillary force can facilitate more accurate segmentation of blood vessels.

Segmentation of neighboring organs in medical image with model competition.

This paper presents a novel approach for image segmentation by introducing competition between neighboring shape models. Our method is motivated by the observation that evolving neighboring contours should avoid overlapping with each other and this should be able to aid in multiple neighboring objects segmentation. A novel energy functional is proposed, which incorporates both prior shape information and interactions between deformable models. Accordingly, we also propose an extended maximum a posteriori (MAP) shape estimation model to obtain the shape estimate of the organ. The contours evolve under the influence of image information, their own shape priors and neighboring MAP shape estimations using level set methods to recover organ shapes. Promising results and comparisons from experiments on both synthetic data and medical imagery demonstrate the potential of our approach.