A study on detection of diseases using Digital Image Processing Techniques with a case study on MRI images and x-ray images of animals

Image processing techniques plays important role in animalmedical image or picture to demonstrativeand recognition the disorders and screen the animal(patient) from these infections. The imageprocessing utilizing as a part of numerous applications in the medicinal picture like MagneticResonance Imaging (MRI), Computerized Topography (CT), ultrasound imaging and X-ray imagesetc The x ray or CT or MRI are it may be on fracture of bone , hip joint, vertibrai column ,Nmemonia , Fermur joint, plating, Intenstine and stomach, neck , Lungs, Periodically heart sizecalculating , Fixing rod etc. These images highly exception cost to the diseased animal(patient) when itdo not clear the reimaging is more cost for that, then the picture operation is one of picture preparingstrategies to take care of this issue by less cost and quick. Animal Medicinal pictures are normallycorrupted by commotion amid picture procurement and transmission process. The primary reasonfor the commotion lessening strategy is to remove noise by holding the essential component ofthe pictures. In a run of the mill arrangement of WSN, hubs are battery worked each of thetherapeutic imaging gadgets are influenced by various sorts of commotion. For instance, the x-rayimages are regularly ruined by Poisson commotion, while the ultrasound pictures are influenced bySpeckle noise. Dot is an intricate marvel, which debases picture quality with a back scattered waveappearance which begins from numerous tiny diffused reflections that going through inside organsand makes it more troublesome for the on looker to segregate fine detail of the pictures in analyticexaminations. Accordingly, denoising or lessening this dot commotion from a loud picture hasturned into the dominating stride in medicinal image processing. Magnetic resonance imaging (MRI)of brain is a noninvasive medical imaging technique used in measuring and visualizing the brain’sanatomical structure, analyzing brain abnormalities, delineating pathological regions, surgicalplanning and image guided interventions. X-ray Different image processing techniques are appliedto MRI images for identification, detection and classification of brain diseases as well asabnormalities. In this thesis, we propose an improved classification method, which can detect braintumors and classify as normal and abnormal brain MRI images. The abnormal brain MRI imagesare also classified as Benign and Malignant on the basis of cancerous or non-cancerous state ofbrain tumors. The classification method is developed by using feature extraction process to extractthe features of brain MRI images for detecting the abnormalities and brain tumors. In our proposedmethod, log-polar transformation (LPT) based feature extraction method is constructed to extractthe features from brain MRI images which are performed as detection factor of brain abnormalities.Kernel support vector machine (KSVM) is applied as image classification tools for the tested brainMRI images. Extensive experiments are simulated over different orientation and conversion ofscales in T-1 and T-2 weighted MRI images. A comparative analysis of the proposed method withother existing algorithm is then performed that clarify the significant improvement of the proposed method.