Adel Hafiane

Median binary pattern for textures classification

A texture classification method using a binary texture metric is presented. The method consists of extracting local structures and describing their distribution by a global approach. Texture primitives are determined by a localized thresholding against the local median. The local spatial signature of the thresholded image is uniquely encoded as a scalar value, whose histogram helps characterize the overall texture. A multi resolution approach has been tried to handle variations in scale. Also, the encoding scheme facilitates a rich class of equivalent structures related by image rotation. Then, we demonstrate - using a set of classifications, that the proposed method significantly improves the capability of texture recognition and outperforms classical algorithms.

Fuzzy Clustering and Active Contours for Histopathology Image Segmentation and Nuclei Detection

Histopathology imaging provides high resolution multispectral images for study and diagnosis of various types of cancers. The automatic analysis of these images can greatly facilitate the diagnosis task for pathologists. A primary step in computational histology is accurate image segmentation to detect the number and spatial distribution of cell nuclei in the tissue, along with segmenting other guiding structures such as lumen and epithelial regions which together make up a gland structure. This paper presents a new method for gland structure segmentation and nuclei detection. In the first step, fuzzy c-means with spatial constraint algorithm is applied to detect the potential regions of interest, multiphase vector-based level set algorithm is then used to refine the segmentation. Finally, individual nucleus centers are detected from segmented nuclei clusters using iterative voting algorithm. The obtained results show high performances for nuclei detection compared to the human annotation.

One dimensional local binary pattern for bone texture characterization

The evaluation of osteoporotic disease from X-ray images presents a major challenge for pattern recognition and medical applications. Textured images from the bone microarchitecture of osteoporotic and healthy subjects show a high degree of similarity, thus drastically increasing the difficulty of classifying such textures. In this paper, we propose a new method to separate osteoporotic cases from healthy controls, using texture analysis. The idea consists in combining global and local information to better capture the image characteristics. Global information is characterized by image projection which conveys information about the global aspect of the texture. Local information is encoded by the local patterns using neighborhood operators. The proposed technique is based on the local binary pattern (LBP) descriptor which has been classically applied on two dimensional (2D) images. Our algorithm is a derived solution for the 1D projected fields of the 2D images. Experiments were conducted on two populations of osteoporotic patients and control subjects. Compared to the classical LBP, the proposed approach yields a better classification rate of the two populations.

Histopathology Tissue Segmentation by Combining Fuzzy Clustering with Multiphase Vector Level Sets

High resolution, multispectral, and multimodal imagery of tissue biopsies is an indispensable source of information for diagnosis and prognosis of diseases. Automatic extraction of relevant features from these imagery is a valuable assistance for medical experts. A primary step in computational histology is accurate image segmentation to detect the number and spatial distribution of cell nuclei in the tissue, along with segmenting other structures such as lumen and epithelial regions which together make up a gland structure. This chapter presents an automatic segmentation system for histopathology imaging. Spatial constraint fuzzy C-means provides an unsupervised initialization. An active contour algorithm that combines multispectral edge and region informations through a vector multiphase level set framework and Beltrami color metric tensors refines the segmentation. An improved iterative kernel filtering approach detects individual nuclei centers and decomposes densely clustered nuclei structures. The obtained results show high performances for nuclei detection compared to the human annotation.

Rotationally Invariant Hashing of Median Binary Patterns for Texture Classification

We present a novel image feature descriptor for rotationally invariant 2D texture classification. This extends our previous work on noise-resistant and intensity-shift invariant median binary patterns (MBPs), which use binary pattern vectors based on adaptive median thresholding. In this paper the MBPs are hashed to a binary chain or equivalence class using a circular bit-shift operator. One binary pattern vector (ie.smallest in value) from the group is selected to represent the equivalence class. The resolution and rotation invariant MBP (MBP ROT) texture descriptor is the distribution of these representative binary patterns in the image at one or more scales. A special subset of these rotation and scale invariant representative binary patterns termed uniformpatterns leads to a more compact and robust MBP descriptor (MBP UNIF) that outperforms the rotation invariant uniform local binary patterns (LBP UNIF). We quantitatively compare and demonstrate the advantage of the new MBP texture descriptors for classification using the Brodatz and Outex texture dictionaries.

Joint Adaptive Median Binary Patterns for texture classification

This paper addresses the challenging problem of the recognition and classification of textured surfaces given a single instance acquired under unknown pose, scale and illumination conditions. We propose a novel texture descriptor, the Adaptive Median Binary Pattern (AMBP) based on an adaptive analysis window of local patterns. The principal idea of the AMBP is to convert a small local image patch to a binary pattern using adaptive threshold selection that switches between the central pixel value as used in the Local Binary Pattern (LBP) and the median as in Median Binary Pattern (MBP), but within a variable sized analysis window depending on the local microstructure of the texture. The variability of the local adaptive window is included as joint information to increase the discriminative properties. A new multiscale scheme is also proposed in this paper to handle the texture resolution problem. AMBP is evaluated in relation to other recent binary pattern techniques and many other texture analysis methods on three large texture corpora with and without noise added, CUReT, Outex_TC00012 and KTH_TIPS2. Generally, the proposed method performs better than the best state-of-the-art techniques in the noiseless case and significantly outperforms all of them in the presence of impulse noise. HighlightsNew method for texture analysis and classification.Adaptive approach to extract the local patterns is proposed.Combining different information with the joint histogram.New multiscale scheme of the texture cue is proposed.The classification accuracy reach high rates and robustness for impulse noise.

Phase-based probabilistic active contour for nerve detection in ultrasound images for regional anesthesia

Ultrasound guided regional anesthesia (UGRA) is steadily growing in popularity, owing to advances in ultrasound imaging technology and the advantages that this technique presents for safety and efficiency. The aim of this work is to assist anaesthetists during the UGRA procedure by automatically detecting the nerve blocks in the ultrasound images. The main disadvantage of ultrasound images is the poor quality of the images, which are also affected by the speckle noise. Moreover, the nerve structure is not salient amid the other tissues, which makes its detection a challenging problem. In this paper we propose a new method to tackle the problem of nerve zone detection in ultrasound images. The method consists in a combination of three approaches: probabilistic, edge phase information and active contours. The gradient vector flow (GVF) is adopted as an edge-based active contour. The phase analysis of the monogenic signal is used to provide reliable edges for the GVF. Then, a learned probabilistic model reduces the false positives and increases the likelihood energy term of the target region. It yields a new external force field that attracts the active contour toward the desired region of interest. The proposed scheme has been applied to sciatic nerve regions. The qualitative and quantitative evaluations show a high accuracy and a significant improvement in performance.

UAV-Video Registration Using Block-Based Features

We present a new approach aimed at fast multiframe registration of airborne video collected by moving platforms such as unmanned aerial vehicles. Registration is used to enable separating the moving objects from the stationary background, which is similar to estimating the egomotion of the sensor. The proposed registration algorithm is used to match the moving background and remap video frames into a common co-ordinate system in order to stabilize that segment of video. The major modules include dense feature detection, sparse prominent edge and corner-based feature block identification, feature block region matching to extract control points, confidence weighted robust projective transformation estimation and image warping to register a segment of frames within a given temporal window. The proposed method is shown to produce good results with small image registration error.

Clustering initiated multiphase active contours and robust separation of nuclei groups for tissue segmentation

Computer assisted or automated histological grading of tissue biopsies for clinical cancer care is a long-studied but challenging problem. It requires sophisticated algorithms for image segmentation, tissue architecture characterization, global texture feature extraction, and high-dimensional clustering and classification algorithms. Currently there are no automatic image-based grading systems for quantitative pathology of cancer tissues. We describe a novel approach for tissue segmentation using fuzzy spatial clustering, vector-based multiphase level set active contours and nuclei detection using an iterative kernel voting scheme that is robust even in the case of clumped touching nuclei. Early results show that we can reach a 91% detection rate compared to manual ground truth of cell nuclei centers across a range of prostate cancer grades.

A new supervised evaluation criterion for region based segmentation methods

We present in this article a new supervised evaluation criterion that enables the quantification of the quality of region segmentation algorithms. This criterion is compared with seven well-known criteria available in this context. To that end, we test the different methods on natural images by using a subjective evaluation involving different experts from the French community in image processing. Experimental results show the benefit of this new criterion.