Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection

A NOVEL ALGORITHM BASED ON CASCADING OF NEURAL NETWORK MODELS AND WAVELET TRANSFORM FOR IMAGE ENHANCEMENT.

Image enhancement and restoration is pre-request of computer vision. The distortion and degradation of image suffered the process of pattern matching and quality of image. Wavelet is very important transform function play a role in image enhancement and image de-noising. The concept of wavelet used as soft thresholding and hard thresholding. A processing of data through wavelet is very efficient in process of neural network. In this paper we discuss the proposed algorithm for image enhancement based on self organized map network and wavelet transform. Basically self organized map network is unsupervised training mechanisms of pattern, due to this reason the processing of network is very fast in compression of another artificial neural network method. And the combination of wavelet and self organized map network have great advantage over conventional method such as histogram equalization and multi-point histogram equalization and another conventional technique of image enhancement

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

A New Method for Gray Level Image Thresholding Using Spatial Correlation Features and Ultrafuzzy Measure

One of the most recent techniques employed to estimate an optimal threshold of a gray level image for segmentation is ultrafuzzy measures. In this paper, we introduce relative fuzzy membership degree (RFMD) taking spatial correlation among the pixels in the image into account. We also propose a novel thresholding technique by combining two-dimensional histogram, which was determined by using the gray value of the pixels and the local average gray value of the pixels using ultrafuzziness and RFMD. Compared to fuzzy membership degree, RFMD of type-II fuzzy sets and ultrafuzzy measure is able to better segment critical gray level images. It was observed that the outcome is so encouraging in objective and subjective perspectives over the existing method for all varieties of images

Automatic Blood Vessel Extraction of Fundus Images Employing Fuzzy Approach

Diabetic Retinopathy is a retinal vascular disease that is characterized by progressive deterioration of blood vessels in the retina and is distinguished by the appearance of different types of clinical lesions like microaneurysms, hemorrhages, exudates etc. Automated detection of the lesions plays significant role for early diagnosis by enabling medication for the treatment of severe eye diseases preventing visual loss. Extraction of blood vessels can facilitate ophthalmic services by automating computer aided screening of fundus images. This paper presents blood vessel extraction algorithms with ensemble of pre-processing and post-processing steps which enhance the image quality for better analysis of retinal images for automated detection. Extensive performance based evaluation of the proposed approaches is done over four databases on the basis of statistical parameters. Comparison of both blood vessel extraction techniques on different databases reveals that fuzzy based approach gives better results as compared to Kirsch’s based algorithm. The results obtained from this study reveal that 89% average accuracy is offered by the proposed MBVEKA and 98% for proposed BVEFA

Texture Image Segmentation using Morphology in Wavelet Transforms

One of the essential and crucial steps for image understanding, interpretation, analysis and recognition is the image segmentation. This paper advocates a new segme- ntation scheme using morphology on wavelet decomposed images. The present paper provides a good segmentation on natural images and textures by dividing an image into non overlapping regions, which are homogenous in terms of certain features such as texture, spatial coordinates etc. using simple morphological operations. Morphological enhancement technique based on Top Hat transforms enhances the local contrast in this paper. The morphological treatment and followed by Otsu2019;s threshold overcomes the problem of noise and thin gaps, and also smooth the final regions. The experimental results on four different databases demonstrate the success of the proposed method, compared to many other methods