Gray Texture Research Articles

Software agent with reinforcement learning approach for medical image segmentation

Many image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%.

Image Retrieval System Based on Adaptive Color Histogram and Texture Features

This paper proposes three image features for use in image retrieval. The first image feature is based on color distribution and is called an adaptive color histogram (ACH). The second and third image features, called adaptive motifs co-occurrence matrix (AMCOM) and gradient histogram for adaptive motifs (GHAM), are based on color and texture features, respectively. ACH uses three steps, including cluster drop, auto cluster merge and auto cluster split to improve the K-mean algorithm. AMCOM is commonly used and is a generally conventional AMCOM. GHAM estimates the average gradient of pixels in a motif of pattern blocks and creates a histogram of the average gradient corresponding to motifs of pattern blocks. We integrate ACH, AMCOM and GHAM to form an AMCGH system to facilitate a more accurate and efficient image retrieval system. To present the performance of the proposed system, four different image databases, including gray texture images, two types of color texture images and color natural images, are tested and analyzed.

A new approach for classification of human brain CT images based on morphological operations

Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features, i.e. gray scale, symmetry and texture. Some classifiers were applied to classify normal and abnormal brain CT images. It showed that morphological operations can improve the result of accuracy. Moreover SVM classifier showed better result than other classifiers.

An Efficient Hillclimbing-based Watershed Algorithm and its Prototype Hardware Architecture

Image segmentation is the process of isolating objects in an input image, that is, partitioning the image into disjoint regions, such that each region is homogeneous with respect to some property, such as gray value or texture. Watershed-based image segmentation has gained much popularity in the field of biomedical image processing and computer vision where large images are not uncommon. Time-critical applications like road traffic monitoring, and steel fissure analysis require fast realization of the segmentation results. The present paper proposes a fast watershed transform based on hillclimbing technique. The complexity of the algorithm has been reduced by doing away with multiplication normally required to form a lower complete image in an intermediate step of the overall segmentation. The reduced complexity makes the algorithm suitable for dedicated hardware implementation. An FPGA-based architecture has been developed to implement the proposed algorithm involving moderate hardware complexity. This architecture enhances the applicability of this algorithm for real-time applications.

Color texture analysis using the wavelet-based hidden Markov model

The wavelet-domain hidden Markov model (WD HMM), in particular the hidden Markov tree (HMT), has recently been proposed and applied to gray texture analysis with encouraging results. For color texture analysis, the previous WD HMM can only be used to model the different color planes individually, assuming they are independent of each other. However, this assumption in general is unrealistic. We show in this paper that the wavelet coefficients have certain inter-dependences between color planes. This paper presents a novel approach to modeling the dependences between color planes as well as the interactions across scales. In our approach, the wavelet coefficients at the same location, scale and sub-band, but different color planes are grouped into one vector. We then propose a multivariate Gaussian mixture model (MGMM) for approximating the marginal distribution of the wavelet coefficient vectors in one scale and capturing the interactions of different color planes. In addition, the statistical dependence between different scales is captured by the transition matrix of the hidden Markov tree. Using this approach, we can improve the performance of the WD HMM on the color texture classification. The experiments show that our WD HMM approach provides 85% of correct classifications (PCC) on 68 color textures from Oulu texture database and outperforms the other wavelet-based methods we study. In this paper, we also investigated the classification performance of the WD HMM methods on different color spaces.

Correspondence of line segments between two perpective images: comparaison between Epipolar, Bayesian and Neuronal approaches

In order to permit the localization and the navigation of a mobile robot within an interior environment, we have built a stereoscopic sensor and implemented all the algorithms which allow to obtain 3D coordinates of real objects from data images. Sensor uses two mini cameras with vertical disposition. Processing on the images leads us to have segments on the two images. To match segments, we have to answer at the question: “Is there two segments, in a pair from top and bottom images segments, are two views of the same part of the real world of the robot ?”. For choosing the best matching algorithm, we compare three approaches: epipolar , Bayesian and neuronal techniques. In the epipolar method, we use only geometrical features. Two images of a single scene/object are related by the epipolar geometry, which can be describe by a 3x3 singular matrix called the fundamental matrix. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and navigation of a mobile robot. With the two others, we associate to each segment, a set of 16 parameters which includes geometrical, gray level, textural and neighborhood features. For the comparison of these approaches, we have built a database of over 2500 segments. Results about this work are presented in our paper. Results with the two techniques from the data classification, are very good. In order to increase the quality of the segments matching, it is possible to find a combination rule for these two approaches. But, even if for the epipolar method 22% segments are not recognized, we think it is interesting to introduce it with the aim to obtain better results in some difficult configurations, by example when gray levels and textures are not very different in the scene, and also in order to initialize the classification algorithm. KEY WORDS: . Global Jnl Pure & Applied Sciences Vol.10(2) 2004: 335-341

An automated method for the quantification and fractal analysis of immunostaining.

Aims. In tissue counter analysis (TCA) digital images of complex histologic sections are dissected into elements of equal size and shape, and digital information comprising grey level, colour and texture features is calculated for each element. In this study we assessed the feasibility of TCA for the quantitative description of amount and also of distribution of immunostained material. Methods. In a first step, our system was trained for differentiating between background and tissue on the one hand and between immunopositive and so‐called other tissue on the other. In a second step, immunostained slides were automatically screened and the procedure was tested for the quantitative description of amount of cytokeratin (CK) and leukocyte common antigen (LCA) immunopositive structures. Additionally, fractal analysis was applied to all cases describing the architectural distribution of immunostained material. Results. The procedure yielded reproducible assessments of the relative amounts of immunopositive tissue components when the number and percentage of CK and LCA stained structures was assessed. Furthermore, a reliable classification of immunopositive patterns was found by means of fractal dimensionality. Conclusions. Tissue counter analysis combined with classification trees and fractal analysis is a fully automated and reproducible approach for the quantitative description in immunohistology.

Textural analysis of IRS-1D panchromatic data for land cover classification

Manifestation of texture in high spatial resolution optical satellite data has an inherent potential to provide land cover information. A study has been conducted to explore this utility with IRS-1D panchromatic (PAN) data through its grey values and the derived textural information. A typical test site with a wide range of textures was subjected to textural analysis by the Grey Level Co-occurrence Matrix (GLCM) approach. Entropy at an Inter Pixel Distance (IPD) of 1 and Correlation at an IPD of 9 were found to be the optimised textural features. The data has been independently classified using PAN grey levels, textural information, and PAN combined texture, which showed significantly improved classification accuracy with the addition of textural information. Similar results were obtained over two other independent sites, validating the consistent performance of the optimised textural features.

Performance analysis of textural features for characterization and classification of SAR images

A new method has been presented to compare the performance of textural features for characterization and classification of SAR (Synthetic Aperture Radar) images. In contrast to the conventional comparative studies based on classification accuracy, this method emphasizes the sensitivity of texture measures for grey level transformation and multiplicative noise of different speckle levels. Texture features based on grey level run length, texture spectrum, power spectrum, fractal dimension and co-occurrence have been considered. A number of image samples of built-up, barren land, orchard and sand regions were considered for the study. The interpretation of the results is expected to provide useful information for the remote sensing community, which employs textural features for segmentation and classification of satellite images.

A methodology for evaluation of boundary detection algorithms on breast ultrasound images

Image segmentation is the partition of an image into a set of non-overlapping regions that comprise the entire image. The image is decomposed into meaningful parts, which are uniform with respect to certain characteristics, such as grey level or texture. This study presents a novel methodology to evaluate ultrasound image segmentation algorithms. The sonographic features can differentiate between various sized malignant and benign breast tumours. The clinical experiment can determine whether a tumour is benign or not, based on contour, shape, echogenicity and echo texture. Further study of the standardized sonographic features, especially the tumour contour and shape, will improve the positive predictive value and accuracy rate in breast tumour detection. The effectiveness of using this methodology is illustrated by evaluating image segmentation on breast ultrasound images. Via definite segmentation, the appreciated tumour shape and contour can be ascertained. Furthermore, this method can enhance the ability of ultrasound to distinguish between benign and malignant breast lesions.

The Use of Boolean Model for Texture Analysis of Grey Images

We generalize here the use of the 1D Boolean model for the analysis of grey level textures. Each grey image is first split into eight binary images using different criteria. Each of these binary images is separately analysed with the help of the 1D Boolean model and features are extracted from it. The final grey texture recognition is performed on the basis of these features using several classification criteria. Experiments have been carried out using an image database of 30 grey level textures, all of them with 512×512 pixels in size, obtaining correct classification rates between 95% and 100%, according to the classification criterion used.

Developing a quantitative model of human preattentive vision

To develop robust computer vision system methodologies, ones that have a range of applicability, early vision operators capable of matching a level of human perceptual performance are required. This implies the need to develop operators that can perform a variety of image analysis tasks in a unified and consistent fashion. These image analysis tasks include finding boundaries between regions of uniform but different gray levels and textures. They also include gauging Gestalt grouping concepts such as uniformity and proximity, so that these concepts are incorporated into the segmentation process. Lastly, the operators should be able to gauge information that allows the characteristics of surfaces to be made explicit, e.g., so-called shape-from-shading and shape-from-texture methods. Developing robust methods for performing these tasks corresponds to developing a quantitative model of human preattentive vision. A basis for such a quantitative model is proposed that is contrary to current theories in computer vision but that seemingly provides a unified method for image analysis tasks and to explain a number of perceptual phenomena. >

Texture synthesis using a growth model

A systematic method of gray tone texture generation is presented. The procedure is composed of two phases. The first phase is to generate a set of images having characteristic pattern structures. Called mother images, these are synthesized by repetitive applications of seed distribution operations, skeleton growth operations, and muscle growth operations. The second phase is to synthesize a set of gray tone textures from a mother image by applying specific probabilistie transformations which convert numbers in the mother image to gray tone values.

Nuevo centro de investigación y administración en Hayes Park, Inglaterra

This project includes two buildings, each of three storeys. One of them houses the administration offices, and is 60.96 x 48.77 m in surface dimensions. It accomodates 450 staff members. The other, which is 48.77 by 32.92 m in planform, has a research department, laboratories, lecture hall and auxiliary services. The structure of this building is noteworthy, and is made with precast reinforced concrete elements. The perimeter supports have an audacious design, and their rough, grey texture is left uncovered. This new Centre of Research and Administration, which gives the observer a sensation of elegant simplicity, has contributed to maintain and emphasize the rural quality of the environment.

On Chelonian Scutes from the Stonesfield Slate

In the British Museum there are several specimens of “papilionaceous” flattened bodies exhibiting six or more concentric lines radiating from two nuclei on either side of an elevated medial line. The stone in which they are embedded is unquestionably the Stonesfield slate; it contains the characteristic Trigonia angulata, Rhynchonella, Ostrea, and Modiola Sowerbyana, D'Orb. (= plicata, Sowerby). The enigmatical bodies to which I now allude are stained of a deep redferruginous colour, the matrix retaining the grey tint and crystalline texture of the Stonesfield slate. On comparison between these remains and those of the specimens of Geomyda spinosa, from Singapore, in the British Museum, presented by Sir A. Smith, a comparison which was suggested to me by Mr. Davies, whose accurate discrimination first threw light upon the nature of the present evidence, I have been led to consider that the specimens in the Fossil Gallery represent the second, third, and fourth median scutes of a tortoise allied to the recent African species. The fossils and their corresponding impressions from the Stonesfield slate afford, according to my interpretation, evidence of the texture of the horny scutes which were developed outside the bony carapace of the old Oolitic tortoise. A particular interest is attached to these specimens, as they were considered by the late Edward Forbes as Trigonellites, or opercula of Ammonites.