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This paper presents a new method to characterize the texture of gray level bone radiographic images. The technique is inspired from the Local Binary Pattern 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. The method requires a series of preprocessing of images. A clinical study is led on two populations of osteoporotic and control patients. The results show the ability of our technique to better discriminate the two populations than the classical LBP method. Moreover, they show that the structural organization of bone is more anisotropic for the osteoporotic cases than that of the control cases in accordance with the natural evolution of bone tissue linked to osteoporosis. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of fire color spaces and the evaluation of image segmentation methods commonly available in the literature of wildland and urban fires. The evaluation method, based on the determination of a segmentation quality index, is applied on three series of fire images obtained at the usual scales of validation of forest fire models (laboratory scale, fire tunnel scale and field scale). Depending on the considered scale, different methods reveal themselves as being the most appropriate. In this study we present the advantages and drawbacks of different segmentation algorithms and color spaces used in fire detection and characterization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 uniform patterns 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A modified FCM algorithm based on spatial and multiresolution constraints is described in this paper. First the pyramid is built from the original image then in each level FCM parameters are computed under a neighborhood spatial constraint. The coarse membership functions propagate down to fine layers to improve segmentation accuracy. The algorithm is tested on both synthetic and multispectral images. Experimental results are presented, showing the effectiveness of the method. [ABSTRACT FROM AUTHOR]