Though a popular imaging technique, ultrasound is known for producing images filled with noise, distortions and shadowing effects. As a result, segmentation of ultrasound images require significant prior knowledge, often inserted into algorithms interactively or through shape information of the region of interest. This type of prior knowledge puts limitations on current approaches. This paper presents a different approach to ultrasound image segmentation that relies mainly on the physical properties of ultrasonic imaging. Robust intensity-based external energy formulations are incorporated into an Active Contour framework that is tolerant of the noise common to ultrasound images. By initializing the contour through an ellipse fitting procedure, an autonomous ultrasound image segmentation system is created that that can generalize to objects of varying shapes and sizes. The segmentation system was tested on ultrasound images of neonatal kidneys with results comparable to current methods.
Data Mining and Machine Learning plays most inspiring area of research that become most popular in health organization. It also plays a vital part to uncover new patterns in medicinal science and services association which thusly accommodating for all the parties associated with this field. This project intend to form a diagnostic model of the common diseases based on the symptoms by using data mining technique such as classification in health domain. In this project, we are going to use algorithms like Random forest, Naive Bayes which can be utilized for health care diagnosis. Performances of the classifiers are compared to each other to find out highest accuracy. This also helps us to find out persons who are affected by the infection. The test based on the outcomes of the diseases.
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