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Since the parameters used for OA classification are continuous, human experts may differ in their assessment of OA, and therefore reach a different conclusion regarding the presence and severity. This introduces a certain degree of subjective ness to the diagnosis [], and requires a considerable amount of knowledge and experience for making a valid OA diagnosis.

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Due to the high prevalence of OA, there is an emerging need for clinical and scientific tools that can reliably detect the presence and severity of OA. Researchers proposed a computer-aided method of grading hip Osteoarthritis based on textural and shape descriptors of radiographic hip joint space, and showed Someone [7] proposed a system that monitors for changes in finger joints based on a set of radiographs taken at different times, which can detect changes in the number and size of osteophytes.

However, despite the prevalence of knee OA, computer-based tools for OA detection based on single knee X-ray images are not yet available for either clinical or research purposes. Here we describe a method for edge detection of OA by using computer-based image analysis of knee X-ray images. While at this point we do not suggest that the proposed method can completely replace a human reader, it can serve as a decision-supporting tool, and can also be applied to the classification of large numbers of X-rays for clinical research trials. Two common clinical signs of knee disease are patella alta and patella baja [], which can be evaluated by referring to the patella position on a lateral knee x-ray image.

The signs are clinically important because it was reported to correlate with patellofemoral alignment and changes in contact area during weight bearing that might result in chondromalacia []. This manual process is laborious and can take up to 1 hour for each patient scan. It is also subject to the judgement of the clinician and requires significant experience and training to produce accurate and reproducible results.

Osteoarthritis is the most common form of arthritis and involves the gradual loss of articular joint cartilage. Recent studies have shown that the quantitative measurement of knee cartilage volume is an accurate and reproducible method for the measurement of osteoarthritis progression [19].

Current methods of cartilage volume measurement involve some form of manual segmentation carried out by a trained clinician. The key steps in the segmentation process involve delineating the cartilage and separating it from the surrounding tissues. The scans obtained are usually grey scale images in the sagittal plane and consists typically of 60 images slices for each knee.

Early-developed methods related to automatic landmark localization on x-ray images can be roughly divided into two categories: neural network-based and model-based methods.

Researchers presented several neural network-based localization methods for cephalometric landmarks from x-ray images based on different machine learning approaches, including neuro-fuzzy system, multi-layer perceptron and least-squares function approximator. These learning systems were trained to learn the spatial relation between some predefined geometric features e.

The system, after training, can then be applied to an input image for estimating the landmark locations. Nevertheless, the performance of neural network-based methods quite depends on predefined geometric features. As the knee is an articulated structure, each knee bone segment supports an individual way of movement, and furthermore, the ISR is related to the landmarks on more than one bone segment.

The relation between the knee structure and landmark locations among different lateral x-ray images is thus unsteady due to various knee poses. Selecting geometric features for consistently describing the relation hence becomes quite challenging, making the neural network-based methods less appropriate for localizing landmarks on articulated structures.

The active shape model ASM introduced by Cootes and Taylor [] and was a typical model-based method for localizing anatomical landmarks. The ASM represented a target structure by a parameterized statistical shape model obtained from training. By iteratively fitting the shape model to the boundary of the target object, the locations of desired landmarks can be estimated. The ASM-based methods are similar to the neural network-based methods in some sense; in order to achieve a stable localization process, both of them acquire some prior knowledge, implying the relations between specific geometric features and landmark locations through a learning process.

Unfortunately, as the solutions are searched only in a local neighbourhood while using the ASM-based methods, these methods are very sensitive to the pose of the initial model. If the shape model is initialized far from the object of interest, the searching process tends to be slow or even fail. Most of the research on anisotropic diffusion over the years has been focused on the preservation of features in the image while denoising the image data.

Anti-geometric diffusion is a form of anisotropic diffusion that goes against this trend by diffusing across image edges, in a direction orthogonal to the geometric heat flow. Geometric heat flow diffuses along image edges, thus preserving the edges while anti-geometric diffusion effectively spreads out the edge information. The method is thus termed anti-geometric because it is orthogonal to the geometric heat flow.

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The advantage of smearing edge information is that it allows quick detection of features and their location within an image, thus enabling fast segmentation of the image. Image regions that lie nearby, but on opposite sides of a prominent edge are quickly distinguished. Edge directions are usually related to the tangents of the isointensity contours level curves , since the tangent direction of an isointensity contour is the direction perpendicular to the image gradient.

In this paper we have proposed a new method for detecting edge rather than using one of the known methods of edge detection. In the first step of our proposed method we perform horizontal scanning. If any change of pixel intensity is observed it is marked by a black pixel indicating a horizontal edge point. In this instance, the original authors of the worm study had taken the brave and unusual step of making their data widely available. That, ironically, exposed their work to far greater scrutiny than is applied to most studies.

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