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This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification(More)
  • J. Jacqulin, Margret B. Lakshmipathi, +13 authors D. Selvathi
  • 2012
Thyroid gland secretes thyroid hormones to control the body's metabolic rate. The malfunction of thyroid hormone will leads to thyroid disorders. The under-activity and over-activity of thyroid hormone causes hypothyroidism and hyperthyroidism. This paper describes the diagnosis of thyroid disorders using decision tree attribute splitting rules. Since,(More)
Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal magnetic resonance (MR) images. Fuzzy clustering using fuzzy C-means (FCM) algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge(More)
Automated eye disease identification systems facilitate the ophthalmologists in accurate diagnosis and treatment planning. In this paper, an automated system based on artificial neural network is proposed for eye disease classification, Abnormal retinal images from four different classes namely non-proliferative diabetic retinopathy (NPDR), Central retinal(More)
In medical image visualization and analysis, segmentation is an indispensable step in the processing of images. MR has become a particularly useful medical diagnostic tool for cases involving soft tissues, such as in brain imaging. The aim of our research is to develop an effective algorithm for the segmentation of the MRI images. This paper discusses the(More)
In the conventional and relatively simple image processing techniques are most important task in the field of medical imaging. In this work to provide information about segmentation and classification methods that are very important for medical image processing. Ultrasound is unique in its ability to image patient anatomy and physiology in real time,(More)
Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year. The chances for liver cancer in men and women have increased to 40% and 23% respectively. Segmentation of liver from images of the abdominal area is critical for diagnosis of tumor and for surgical procedures. Accurate detection of the type of the liver(More)
The clinical reports usually offer morphometric data in terms of change relative to a prior study. Therefore, to provide the information about an object clinically in terms of its size and shape, image segmentation and classification are important tools in medical image processing. Ultrasound is a versatile imaging technique that can reveal the internal(More)
Image segmentation is an important technique for image processing which aims at partitioning the image into different homogeneous regions or clusters. Lots of general-purpose techniques and algorithms have been developed and widely applied in various application areas. For the study of anatomical structures and to identify the region of interest. Magnetic(More)