At the Computer Science department office
Appointment on Visitation important
Topic: Computer Vision Image Processing And Information Security
Description: Computer Vision Image Processing Image processing is a way of performing certain actions on images. This is achieved through the use of algorithms. The first task of image processing is enhancement of acquired digital image which helps to put it in a form that best suites a particular application it is intended for. Different techniques such as Median Filtering, Histogram Equalization, Noise Removal, Linear Contrast Adjustment, Decorrelation Stretch with no specific one recommended for particular application. This is because a technique which gives best performance for an application may not do the same for another application. The commonest approach used is Histogram Equalization which operates by transforming an image into a uniform histogram. Region of interest is then carved out of the image through a process called segmentation. Useful information is then extracted for further analysis. This is done through projection from high dimensional space to a lower dimensional space which helps to reduce processing time. Images obtained through Computed Tomography and Magnetic Resonance Imaging scans as well as those obtained from biometric traits can be processed.Information SecurityThis is intended to keep data protected from unauthorized persons.The need to keep information safe has become increasingly important. The basic components of information security are: Confidentiality, Availability, and Integrity. Different organization, system-specific and issue-specific policies are implemented using different methodologies. One of such methodologies is penetration testing which is a way of testing computer systems, network applications in order to identify vulnerabilities that an attacker could capitalize on.
|Post Graduate Program Certificate (1 Artificial Intelligence and Machine Learning: Business Applications 2 Data Science and Business Analytics)
|The University of Texas, Austin, USA
COMPARATIVE ANALYSIS OF CIRRHOTIC AND NON-CIRRHOTIC LIVER USING CT LIVER VOLUMETRY
Background to the StudyMedical experts are able to assess the extent of organs defects based on their knowledge of normal appearances of such structures. A way of distinguishing between normal and abnormal appearances of organs is through volumetry assessment. Cirrhosis is a liver disease which develops slowly over a period of time. At the initial stage of its development, no symptoms are usually noticed but as the situation gets worsened, symptoms begin to manifest, effects of which can be very devastating.Problem statementIdentification of liver with cirrhosis is normally done by human experts; this could be subjective or prone to error. So a computerized approach which enables liver volumes to be extracted will remove subjectivity and increase accuracy. Aim and ObjectivesThe aim of the research work is to characterize liver based on volume and the objectives are as follows: 1 To segment liver 2 To measure the volume of liver 3 To compare liver with cirrhosis and the control groupSignificanceThe study will enable early diagnosis of liver cirrhosis, increased life expectancy, and easy diagnosis of liver cirrhosis. It will serve as a novel quantitative approach to diagnosis of liver cirrhosis.MethodologyData to be used for this work was acquired from SIEMENS scanner in DICOM format. The scanner is a 64 slice CT type used to take scan of the abdomen. . When segmenting an image, region based approach would be used. The evaluation metric to be used for measuring the quality of segmentation is Jaccard Index.The volume of the segmented liver is computed using matlab codes. The comparisons of liver volumes will be done on group and gender basis using independent t-test.The expected resultLiver volumes of patients with decompensated cirrhosis will be significantly lower than those of normal healthy controls.
AIYENIKO OLUKAYODE is a Lecturer I at the Department of Computer Science
AIYENIKO has a Post Graduate Program Certificate in 1 Artificial Intelligence and Machine Learning: Business Applications 2 Data Science and Business Analytics from The University of Texas, Austin, USA