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LLB graduate Lizandré Mulder excels in law and athletics

21 April 2022 | Story Lunga Luthuli | Photo Supplied

Moving from Jansenville – a town outside Uitenhage – to Bloemfontein for her LLB studies, things got off to a shaky start for Lizandré Mulder. New in a ‘big town’, the ‘country girl’ felt out of her element and not used to big-city life. Thanks to her lecturers, the journey to a legal qualification at the University of the Free State (UFS) ended with an average final-year mark of 80% for the Law graduate.

Back in Jansenville, Lizandré’s neighbour nicknamed her ‘klein prokureurtjie (little lawyer)’ as she was growing up, because she had a ‘habit of arguing’, which motivated her to choose law as a career. She says, “arguing with facts earlier, has turned into a passion”. “The competitive side of me always wants to win; I guess that makes me the perfect candidate for a future advocate,” she says.

Managing undergraduate studies, Lizandré – who is also an accomplished athlete – says all she did was study and train. “The only thing I struggled with was my sleeping schedule, as I was constantly tired from hard training, and I studied till the morning hours while I had to wake up again early for morning training.”

The track, field, and cross-country runner has received numerous national medals for the sport and will unfortunately miss the invitation to the annual Excellence Awards in the Faculty of Law, as she will be competing in this year’s South African Athletics Championships in Cape Town on 22 April 2022.

Graduating with the LLB degree, Lizandré plans to finish her master’s degree with a possible topic on the legality of human gene editing in South Africa for the purposes of disease treatment or the prevention thereof.

Lizandré does not believe in having a role model, but to “always try to better myself in every aspect of life. I always believed that true inspiration and motivation come from within”.

After completing her master’s degree, Lizandré will decide on her future career path. She says: “I am still deciding whether I want to remain in Bloemfontein or relocate to Potchefstroom, as the latter has a law firm specialising in medical negligence, a field I would like to specialise in. Besides this, the two cities also boast the best athletics coaches in DB Prinsloo, Head of KovsieSport, and Jean Verster in Potchefstroom has mentored South African award-winning runner, Caster Semenya.

“Somewhere in the future, I definitely also plan on doing my doctoral degree in Law,” says Lizandré.

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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