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11 May 2022 | Story Dr Nitha Ramnath
Belinda Viljoen
Belinda Janeke

Belinda Janeke is the proud recipient of the Dean’s Medal for best honours student in Industrial Psychology in the Faculty of Economic and Management Sciences (EMS), which was awarded during the recent April graduation ceremonies. Janeke, a career adviser specialising in career services with a focus on career development and work-readiness programmes, is currently studying for a master’s in Industrial Psychology, with a view to becoming a registered industrial psychologist.

“The Dean’s Medal was a huge surprise and shock, and I couldn’t have done it without the amazing lecturers I had,” says Janeke.  “They have done so much to engage with us, teach us, and help us with online teaching in 2020 and 2021.”

Janeke began her career as an orientation officer at the University of the Free State (UFS) in 2009 and was promoted to advising and lecturing before being appointed as Head: Career Services (Student Affairs) in 2013, a position she currently holds.

“I really enjoyed my studies; I could live my passion and apply the work experience I gained over time as well. It is just a super blessing from Above; only by God’s grace that this happened,” explains Janeke.

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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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