Measurement Extraction using Fuzzy Set Rule for Segmented Features of Brain Tumor in T-1 & T-2 Weighted Images

Aims: For neuro radiologist it becomes hard to accumulate features with minute dissimilarity in plenty of cases, so it is hard to make a correct decision. Therefore, the need is to generate some rules for prediction of degree of malignancy in tumors. Design: The pre-operative analysis of brain lesion is based on magnetic resonance imaging and clinical data set. Analysis of MRI finding and medical data set gives the relationship between regular pattern & interpretable pattern to acquire desired degree of malignancy. Until now the edge detection, segmentation and morphological operators are used to detect exact location of brain tumor. As uncertainty exits; here fuzzy set rules are evaluated to predict the degree by which a benign tumor is converted into malignant tumor. Methods: Fuzzy extraction theory has been applied along with image progressing algorithms like edge detection; segmentation and morphological operation based on spectral transformation are used to detect exact location of brain tumor to predict the degree malignancy. Step of Image analysis: a) Preprocessing: input 2D gif or tiff image b) Filtering of image using Anisodiff filter c) Thresholding, applying morphological operators and tumor line detection. Statistical Analysis used: A diagnostic feature includes blood flow, mass effect, temperature, calcification, edema, signal intensity & so on. Numerous features can be taken into consideration for better outcome. Results: Fuzzy set rule is one of the promising methods along with MR finding to achieve accuracy higher than 85% by considering few of the medical symptoms on different features. Conclusions: This research is limited to specific region and type of glioma and thus cannot deal heterogeneous cases in which situation is much complicated. The result evaluated here are usually retroactive. As studied, by analyzing signal intensity of T-1 & T-2 weighted image alone, accuracy of 60-70% has been achieved. So in order to get higher accuracy feature like cyst generation, oedema, blood supply are included to achieve 85% accuracy.

Effect of morphological algorithms on medical imaging

Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment. In this article, we focus on describing the new morphological algorithms by ITK (Insight Segmentation and Registration Toolkit). These morphological operators eliminate noise, detect good edges, and overcome the drawback of traditional edge detection methods.

Morphological analysis of mosaic shapes with directed relationships based on attribute and relational model representations

We introduce attribute and relational representations of mosaic image models with directed relationships between regions. Attribute representations of asymmetric relational models based on stacking, ranking and integral descriptions are considered. We propose some morphological shape similarity measures based on relational models. We show that using the same oriented relational model, various morphological operators can be constructed, in particular, of Serra- or Pyt’ev type. Some constructive methods for the design of such morphological operators in an attribute and relational domains are proposed. From this consideration we also extract a new morophlogical scheme for two-stage mutual adaptive image-and-shape joint filtering: at the first step, the shape is simplified (projected) with regard to the image to be projected, and at the second step, the image is simplified (projected) with regard to the simplified (projected) shape.

MORPHOLOGICAL IMAGE ANALYSIS BASED ON ATTRIBUTE AND RELATIONAL REPRESENTATIONS OF MOSAIC IMAGE MODELS

A unified scheme for morphological analysis based on attribute and relational representations of mosaic image models is proposed. We consider 4 main types of model representation: functional-attribute (2D feature map), functional-relational (4D relational map), structure-resource-attribute (an area list with resources and attributes), and structure-resource-relational (a graph, which nodes correspond to regions and edges – to relations and both having resource attributes). In this case, the forms of representation of the model are equivalent to each other, in the sense that they contain the same information, there is a one-to-one correspondence between them, and the formulas for the transition from one representation to another can be written out explicitly. In this scheme, the construction of specific morphological operator for some complete image model presumes the separation of this model into two parts: the guiding (modifying) part, which determines the transformation algorithm, and the guided (modifiable) part to be transformed. These two parts of the model can intersect, therefore cannot be called “variable” and “constant” components. As a basic sample, we consider the halftone Pyt’ev morphology. We explore the specifics of different-sort models, introduce the mutual models and propose different tools for creation of model-based morphological operators. Further, various other morphological systems can be described and explored using the proposed generalized approach.