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22 September 2021
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Story Michelle Nöthling
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Photo Supplied
Annemarie Le Roux.
“I love working with children.” This is one of the first things Annemarie le Roux mentions when asked to describe herself. This love for children propelled Annemarie into the field of education and she graduated in 2006 with a BEd in Foundation Phase at the UFS. Annemarie immediately immersed herself in the Deaf community, enriching the lives of children at the Thiboloha School for the Deaf in Qwaqwa and the De la Bat School for the Deaf in Worcester.
The academic world enticed Annemarie back to the University of the Free State (UFS) and she was appointed as a junior lecturer in the Department of South African Sign Language (SASL) and Deaf Studies in 2013. Going from strength to strength, Annemarie completed her master’s degree in SASL in 2019, and published an
article earlier this year that she co-wrote with Marga Stander. In this article, they found that SASL “has become an increasingly popular language that hearing university students want to learn as a second language” and subsequently explored different teaching methods used for this emerging group of interested students.
Although now firmly established in academia, Annemarie is still committed to the practical application of SASL. “I am closely involved in student and community engagement through the
SIGNALS Sign Language student association that helps empower the Deaf community and South African Sign Language.” She also interprets for the Deaf community whenever she gets an opportunity, as well as for Deaf students in class and meetings.
On the importance of Sign Language and the recognition of the Deaf community in South Africa, Annemarie believes it will open greater opportunities for development. “More people will be able to learn SASL, and it might even become a subject in school for hearing children.”
Mathematical methods used to detect and classify breast cancer masses
2016-08-10
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.