Image analysis

architecture and details of how that feature is implemented in practice. This prototype architecture is intended to provide a test-bed for research into matching computer architectures to image analysis. One of the difficulties encountered within this work was wide disagreement upon some of the basic principles of image analysis. Given this disagreement on the problem it is difficult to construct an architecture to assist the solution of that problem. Chapters four, five and six describe, in some depth, several image analysis algorithm s and implementations of those algorithms on the proposed architecture. These include two complete image analysis tasks. The first of these tasks simply involves locating and counting the number of particles of varying sizes in an image. The second task is described in more detail and involves the location of objects in the image based on their shape, given a geometric model of the object. This task is at the leading edge of image analysis research. The principle behind the solution given here is to limit the problem to the recognition of two dimensional objects and then tackling that problem in depth to create a more robust system than previous efforts. Study of these tasks then allows a reasonable assessment of the performance o f the machine. Clearly, tasks can be devised that have different requirements, but the tasks tested do have a reasonable range and offer a significant test for the architecture given in chapter three. Chapter seven analyses how this architecture coped with each task. Every important feature of the architecture is examined and the value of it is tested. This finally leads to a much better understanding of the requirements of image analysis and also a better understanding of how these may be m et by computer architectural features.

The use of automated evaluation of digital retinal images has the potential to reduce the workload and thus increase the cost-effectiveness of such screening also some manufactures offer automated clinical decision support system targeting these applications including retinal image analysis tool with diagnostic capabilities. However, there still remain a number of problems that must be overcome in order to develop fully reliable automated retinal images analysis systems. One of these problems is the need to guarantee that the quality of the retinal images to be graded exceeds a threshold below which the automated analysis procedures may fail. This is a real problem for low-quality images as seen in [1], [4] so a computationally efficient algorithm for assessment of retinal image quality of very good performance compared to conventional methods is developed.

Going forward, clinical annotation of histopathology data will be a large bottleneck in the evaluation of histopathology related CAD algorithms. Apart from the time constraints on the pathologist to generate this data, the process should be streamlined with active communication between the image analysis scientists and the clinicians with regard to the sort of annotation required, the format and scale at which the annota- tion is generated, and the ease with which the data can be shared (since histopathology files typically tend to be very large). For instance, the sophistication of annotation required to train a CAD system to distinguish cancerous versus noncancerous regions on pathology images may be very different than the annotation detail required to train a classifier to distinguish grades of cancer. While for the former problem the annotation could be done on a coarser scale (lower resolution), the latter annotation may require explicit segmentation of glands and nuclei, a far more laborious and time consuming process. Due to the large size of pathological images, usually it is not possible to process the whole image on a single-core processor. Therefore, the whole image may be divided into tiles and each tile is processed independently. As a consequence, automatic load balancing in the distribution of the cases to different processors need to be handled carefully [120]. Additionally, the processing can be accelerated even further by the use of graphical processing units (GPUs), cell blades, or any other emerging high-performance architecture [121].

Koch’s laboratory, invented a unique technique to study microorganisms by naked eye. It was microbial cell cultivation in cylindrical plates (now known as Petri dishes) on the surface or within of a gel-like nutrient medium up to visible microbial colonies. Appli- cation of photography significantly facilitated the work with microbiological optical images and many mysteries of the microbial world were deciphered by visual observa- tions. Up to now, characterization of microorganisms as optical objects play substantial role in both basic and applied microbiological research. However, all the methods based on the visual examinations are inevitably subjective, in most cases, they are qualitative, and some quantitative approaches are relatively high time and labor consuming. Moreover, it is impossible to “extract” completely and quantitatively all the diverse in- formation, which an optical image contains, by only vision. This information comprises such features as color (or, physically correctly, the spectral properties) and its spatial distribution; size and shape of individual pieces, their mutual position and number; in some cases, it is glowing (e.g. fluorescence), its intensity and spectral characteristics; dynamics of the features. In the second half of the XX century, fundamentally new ap- proach for dealing with these types of information has been developed. It was computer digital image analysis (CDIA).

Model-Based image segmentation plays a dominant role in image analysis and image retrieval. To analyze the features of the image, model based segmentation algorithm will be more efficient compared to non-parametric methods. In this project, we proposed Automatic Image Segmentation using Wavelets (AISWT) to make segmentation fast and simpler. The approximation band of image Discrete Wavelet Transform is considered for segmentation which contains significant information of the input image. The Histogram based algorithm is used to obtain the number of regions and the initial parameters like mean, variance and mixing factor. The final parameters are obtained by using the Expectation and Maximization algorithm. The segmentation of the approximation coefficients is determined by Maximum Likelihood function. It is observed that the proposed method is computationally efficient allowing the segmentation of large images and performs much superior to the earlier image segmentation methods.

unsuccessful results. It is generally known that no more than three diagnostic pulse sequences are usually used to acquire MR images. In this case, we are limited to only three spectral band images for MR multispectral analysis and the ICA to be dealt with is actually over-complete ICA (OC-ICA) as op- posed to under-complete ICA commonly used in the fMRI. Therefore, assuming that the number of sensors is greater than or equal to the number of sources to be separated, as Nakai et al. did in [18] to make the ICA under-complete, is not realistic. The experiments conducted in the previous sec- tions clearly demonstrated serious flaws resulting from the lack of band images and the use of random initial projection vectors by an ICA algorithm. Surprisingly, these interesting issues are very important for the OC-ICA to be used as an MR multispectral image analysis technique, but have never been addressed and explored in the past. To the authors’ best knowledge, this paper is believed to be the first work to investigate the utility of the OC-ICA in MR multispec- tral image analysis. The proposed OC-ICA coupled with a feature extraction-based classification technique as post OC- ICA processing has yielded two major advantages. It makes use of the ICA to linearly transform three band MR im- ages into three statistically independent component images so that these three ICA-generated independent components (ICs) can be stacked one atop another to form a new im- age cube which is spectrally and statistically independent in ICs. As a result, brain tissue substances that appeared in these three component images are supposed to be statistically in- dependent or least dependent from a statistical point of view and can be classified separately and individually to avoid po- tential confusion that may be caused by correlation among these substances when MR images processed an image cube as a whole without an ICA transform. The clear evidence of this advantage was witnessed in our

Abstract: Image processing Technology are proposed to quantify crack patterns .On the basis of the technologies, a software “Crack Image Analysis System” (CIAS) has been developed . An image of soil crack network is used as an example to illustrate the image processing technologies and the operations of the CIAS. The quantification of the crack image involves the following three steps :image segmentation ,crack identification and measurement. First, the image is converted to a binary image using a cluster analysis method; noise in the binary image is removed; and crack spaces are fused. Then, the medial axis of the crack network is extracted from the binary image, with which nodes and crack segments can be identified. Finally, various geometric parameters of the crack network can be calculated automatically, such as node number, crack number, clod area, clod perimeter, crack area, width, length, and direction. The threshold used in the operations is specified by cluster analysis and other innovative methods. As a result, the objects (nodes, cracks and clods) in the crack network can be quantified automatically

A b s t r a c t. Identification and description of the size and shape characteristics of irregular and branched macropores helps the evaluation of the transmission functions of soil. Such macropores are usually formed by tillage operations. In this paper we present a methodological approach, based on the Aphelion image analysis package, to the identification of macropores and quantification of their size and shape characteristics such as surface area, perimeter, circularity, minimum bounding rectangle fill (MBR-Fill) and compactness of resin impregnated opaque sections. This approach includes division of branched macropores into smaller pores if the bottlenecks between them are narrow, assuming that they act independently. Two approaches were used to quantify pore radius from pore surface area (geometrical radius) and from the ratio of pore surface and perimeter (hydraulic radius). The first approach gave more reasonable results, because the hydraulic radius seems to be often poorly sensitive to the pore surface area. Moreover, the pore radius was calculated from the perimeter and it was considerably lower than that determined from the pore surface area. However, taking into account some pore shape characteristics improves the correlation between both radii. The presented approach may help in predicting transmission functions of soil.

Since they were introduced by Hu [7] in 1962, moment methods have attracted considerable attention from researchers. The desirable properties of being invariant to image scaling, translation, and rotation promote the moment-based descriptors, defined in either the circle or rectangle regions, to play significant roles in the scientific fields such as image analysis and pattern recognition. For a general study of moment methods, we refer to [10] [12] [4].

Abstract— Image registration has vital applications in medical image analysis. It is a fundamental preprocessing step where two or more images are aligned into a common coordinate system. Out of various types of registration methods, a popular category is the one, which uses the whole image content to derive a suitable transformation for overlaying the input images. Image registration itself is composed of a number of phases like transformation, interpolation, computing similarity metric and optimization of the transformation parameters (translation, rotation, shearing etc). A major factor that determines the success and effectiveness of any registration method is the optimization strategy we employ for achieving the optimal set of transformation vectors. Hence, it can be viewed as an optimization problem, which computes the geometric as well as intensity transformations at which the input images are having maximal similarity with one another. In this paper, we present a mono modal image registration algorithm for the alignment of T1-weighted MR images of human brain using modified Particle Swarm Optimization (PSO) method for getting the optimum spatial coordinates of the moving image. The experimental results clearly show that the proposed algorithm guarantees better results than the traditional PSO algorithm.

There are three levels in image analysis. In the low level processing, functions that may be viewed as automatic reactions are dealt where as intermediate level processing deals with the task of extracting regions in an image that results from a low level process. High level processing deals with recognition and interpretation tasks. Image analysis is performed using either bottom up approach or top down strategy. In bottom up approach, low level features are extracted from the raw image data and later, this is processed in higher levels. In top down approach, the image characteristics are hypothesized at the highest level and is proceeded towards the lower level until the raw image has been reached (Gonzalez and Woods, 2000; Mantas, 1987). Image analysis involves the study of feature extraction, segmentation and classification (Jain, 1995).

Modern data is captured much faster than it can be analyzed. Novel applications of digital imaging demand super-fast image analysis and recognition. Self-driving vehi- cles, unmanned aerial vehicles (UAV) and autonomous robots are some examples where recognition (of small objects or pedestrians) from video footage must be per- formed in real time. UAVs and underwater robots are restricted in their computational capabilities, as more com- puter power means heavier battery and larger dimen- sions which are at a premium, and therefore sophisticated on-board image recognition algorithms are way too slow for autonomous operation of these devices.

The use of automated evaluation of digital retinal images has the potential to reduce the workload and thus increase the cost-effectiveness of such screening also some manufactures offer automated clinical decision support system targeting these applications including retinal image analysis tool with diagnostic capabilities. However, there still remain a number of problems that must be overcome in order to develop fully reliable automated retinal images analysis systems. One of these problems is the need to guarantee that the quality of the retinal images to be graded exceeds a threshold below which the automated analysis procedures may fail. This is a real problem for low-quality images as seen in [1], [4] so a computationally efficient algorithm for assessment of retinal image quality of very good performance compared to conventional methods is developed.

5. Some instruments can scan and image an entire plate, enabling two-dimensional chromatograms to be evaluated (scan time less than 5 min). The widespread use of planar chromatography means that the applications of spectrodensitometry are almost limitless. Hence, there are extensive publi- cations on the use of scanning densitometry in all types of industry and research. Many of the instru- ment and plate manufacturers also provide applica- tion methods and extensive bibliographies. For example, in all of the following areas scanning den- sitometry has been used for quanti R cation.

Wiener filters are a category of optimum linear filters that involve linear estimation of a desired signal sequence from another connected sequence. It‟s not associated to adaptive filter. The wiener filter‟s main purpose is to cut back the quantity of noise gift in a picture by comparison with associate estimation of the specified quiet image. The Wiener filter may additionally be used for smoothing. This filter is that the mean squares error-optimal stationary linear filter for pictures degraded by additive noise and blurring. It‟s sometimes applied within the frequency domain (by taking the Fourier transform) [17], because of linear motion or unfocussed optics Wiener filter is that the most vital technique for removal of blur in pictures. From an indication process stance. Every component in a very digital illustration of the photograph ought to represent the intensity of one stationary purpose before of the camera. Sadly, if the shutter speed is simply too slow and therefore the camera is in motion, a given component are associate amalgram of intensities from points on the road of the camera's motion.

The textural features can be extracted from the co- occurrence matrix. They are related to specific textural characteristics such as the homogeneity, contrast, entropy, energy and regularity of the structure. In this paper, the texture analysis methods such as, Surrounding Region Dependency Matrix, Spatial Gray Level Dependency Matrix, Gray Level Difference Matrix, Gray Level Run Length Matrix are used to extract the features from the segmented image.Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient result. It is defined as the operation to quantify the image quality through various parameters or functions, which are applied to the original image. Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately.

• We introduce a new semantic-aware image smoothing method. Structure- preserving image smoothing aims to extract image structure from textures and noises. Recently, semantic segmentation has achieved significant progress and has been widely used in many computer vision tasks. We present an interesting observation, i.e. high-level semantic image label- ing information can provide a meaningful structure prior naturally. Based on this observation, we propose a simple yet effective method, which we term semantic smoothing, by exploiting the semantic information to ac- complish semantic structure-preserving image smoothing. We show that our approach outperforms the state-of-the-art approaches in texture re- moval by considering the semantic information for structure preservation. • We present a deep object co-segmentation (DOCS) approach for segment- ing common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on common objects. If multiple object classes are presented in the image pair, they are jointly extracted as the foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architec- ture. The encoder extracts high-level semantic features of foreground objects, a mutual correlation layer detects the common objects, and fi- nally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL dataset with common ob- jects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outper- forms competing methods, for both seen and unseen object classes. • We propose an approach to localize common objects from novel object

EXPERIMENTAL METHOD 30 2.1 PRELIMINARY WORK 30 2.1.1 Test Antihalation Method 2.1 Determine Optimum .2 Processing of Recording Material 2.1.3 Determine Beam Ratios 2.1 .4 Obtain The Diff[r]

Standard CT phantoms, like those proposed by the American Association of Physicists in Medicine [28], allow the evaluation of imaging performance and the as- sessment of how far image quality depends on the adopted technique. Despite not being intended for this, they may provide useful information on the parameters potentially affecting image texture. For instance, a de- crease in slice thickness reduces the photon statistics within a slice (unless mAs or kVp are increased accord- ingly), thereby increasing image noise. The axial field of view and reconstruction matrix size determine the pixel size and hence the spatial sampling in the axial plane, which has an impact on the description of heterogeneity. The reduction of pixel size increases image noise (when the other parameters are kept unchanged), but increases spatial resolution.

As the image is quiet sharp and not clear hence the pixel values of the image is flipped over to obtain a better image (Fig 8) and the corresponding intensity plot is shown in (Fig 9). Comparing Fig 7 and Fig 9 we can easily see the flipped over pixel values.