Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

2D Reconstruction of Small Intestine's Interior Wall

Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.Comment: Journal draf

Automated segmentation of tissue images for computerized IHC analysis

This paper presents two automated methods for the segmentation ofimmunohistochemical tissue images that overcome the limitations of themanual approach aswell as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologie

Coding of details in very low bit-rate video systems

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept ofPeer ReviewedPostprint (published version

Studies on deep learning approach in breast lesions detection and cancer diagnosis in mammograms

Breast cancer accounts for the largest proportion of newly diagnosed cancers in women recently. Early diagnosis of breast cancer can improve treatment outcomes and reduce mortality. Mammography is convenient and reliable, which is the most commonly used method for breast cancer screening. However, manual examinations are limited by the cost and experience of radiologists, which introduce a high false positive rate and false examination. Therefore, a high-performance computer-aided diagnosis (CAD) system is significant for lesions detection and cancer diagnosis. Traditional CADs for cancer diagnosis require a large number of features selected manually and remain a high false positive rate. The methods based on deep learning can automatically extract image features through the network, but their performance is limited by the problems of multicenter data biases, the complexity of lesion features, and the high cost of annotations. Therefore, it is necessary to propose a CAD system to improve the ability of lesion detection and cancer diagnosis, which is optimized for the above problems. This thesis aims to utilize deep learning methods to improve the CADs' performance and effectiveness of lesion detection and cancer diagnosis. Starting from the detection of multi-type lesions using deep learning methods based on full consideration of characteristics of mammography, this thesis explores the detection method of microcalcification based on multiscale feature fusion and the detection method of mass based on multi-view enhancing. Then, a classification method based on multi-instance learning is developed, which integrates the detection results from the above methods, to realize the precise lesions detection and cancer diagnosis in mammography. For the detection of microcalcification, a microcalcification detection network named MCDNet is proposed to overcome the problems of multicenter data biases, the low resolution of network inputs, and scale differences between microcalcifications. In MCDNet, Adaptive Image Adjustment mitigates the impact of multicenter biases and maximizes the input effective pixels. Then, the proposed pyramid network with shortcut connections ensures that the feature maps for detection contain more precise localization and classification information about multiscale objects. In the structure, trainable Weighted Feature Fusion is proposed to improve the detection performance of both scale objects by learning the contribution of feature maps in different stages. The experiments show that MCDNet outperforms other methods on robustness and precision. In case the average number of false positives per image is 1, the recall rates of benign and malignant microcalcification are 96.8% and 98.9%, respectively. MCDNet can effectively help radiologists detect microcalcifications in clinical applications. For the detection of breast masses, a weakly supervised multi-view enhancing mass detection network named MVMDNet is proposed to solve the lack of lesion-level labels. MVMDNet can be trained on the image-level labeled dataset and extract the extra localization information by exploring the geometric relation between multi-view mammograms. In Multi-view Enhancing, Spatial Correlation Attention is proposed to extract correspondent location information between different views while Sigmoid Weighted Fusion module fuse diagnostic and auxiliary features to improve the precision of localization. CAM-based Detection module is proposed to provide detections for mass through the classification labels. The results of experiments on both in-house dataset and public dataset, [email protected] and [email protected] (recall rate@average number of false positive per image), demonstrate MVMDNet achieves state-of-art performances among weakly supervised methods and has robust generalization ability to alleviate the multicenter biases. In the study of cancer diagnosis, a breast cancer classification network named CancerDNet based on Multi-instance Learning is proposed. CancerDNet successfully solves the problem that the features of lesions are complex in whole image classification utilizing the lesion detection results from the previous chapters. Whole Case Bag Learning is proposed to combined the features extracted from four-view, which works like a radiologist to realize the classification of each case. Low-capacity Instance Learning and High-capacity Instance Learning successfully integrate the detections of multi-type lesions into the CancerDNet, so that the model can fully consider lesions with complex features in the classification task. CancerDNet achieves the AUC of 0.907 and AUC of 0.925 on the in-house and the public datasets, respectively, which is better than current methods. The results show that CancerDNet achieves a high-performance cancer diagnosis. In the works of the above three parts, this thesis fully considers the characteristics of mammograms and proposes methods based on deep learning for lesions detection and cancer diagnosis. The results of experiments on in-house and public datasets show that the methods proposed in this thesis achieve the state-of-the-art in the microcalcifications detection, masses detection, and the case-level classification of cancer and have a strong ability of multicenter generalization. The results also prove that the methods proposed in this thesis can effectively assist radiologists in making the diagnosis while saving labor costs

Second-order Shape Optimization for Geometric Inverse Problems in Vision

We develop a method for optimization in shape spaces, i.e., sets of surfaces modulo re-parametrization. Unlike previously proposed gradient flows, we achieve superlinear convergence rates through a subtle approximation of the shape Hessian, which is generally hard to compute and suffers from a series of degeneracies. Our analysis highlights the role of mean curvature motion in comparison with first-order schemes: instead of surface area, our approach penalizes deformation, either by its Dirichlet energy or total variation. Latter regularizer sparks the development of an alternating direction method of multipliers on triangular meshes. Therein, a conjugate-gradients solver enables us to bypass formation of the Gaussian normal equations appearing in the course of the overall optimization. We combine all of the aforementioned ideas in a versatile geometric variation-regularized Levenberg-Marquardt-type method applicable to a variety of shape functionals, depending on intrinsic properties of the surface such as normal field and curvature as well as its embedding into space. Promising experimental results are reported