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06 August 2018 Photo Sonia Small
Karen Lazenby WomenofKovsies
Dr Karen Lazenby strives for a stronger, rule-based, and consistent governance structure.

A transformed University of the Free State (UFS) will be one that promotes social justice in everything it does, a university where its diverse people feel a sense of common purpose and engagement. The UFS is developing this through its Integrated Transformation Plan (ITP) introduced in January 2017. 

“The majority of the current systems and processes in student administration at the university are still manual. This lack of automation leads to inconsistencies and service failures,” says Dr Karen Lazenby. As Registrar for Systems and Administration, Dr Lazenby is responsible for ensuring a smooth and efficient student lifecycle across all three campuses. 

With the ITP, the Governance: Systems and Administration work stream strives to have a stronger, rule-based, and consistent governance structure with a single line of accountability in student administration across all faculties and relevant support departments on the three campuses. By ensuring this ease of use and access there will be an integrated student experience and greater empowerment of students.

“Our focus is on automation and self-services for students (such as the time-table, requests for additional and ad hoc exams and appeals), to ensure transparency and accessibility of rules and policies, decisions relating to admission, progression rules, awarding of qualifications and graduation and faculty and general rules,” Dr Lazenby said.  It will also entail the optimisation of PeopleSoftCampus (the Enterprise Resource Planning system).

“Through this automation, I would also like to get the university’s student administration to such a level that academic staff can focus their energy on teaching and research and student administration staff can focus more on quality assurance,” said Dr Lazenby.

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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