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04 May 2018
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From left: Marné Mentz (second), Ts’epang Sello (first) and Tyler Beling (third) were in a class of their own, taking the first three spots in the 1 500m at the University Sport South Africa national championship in Sasolburg.
As expected, the Kovsie athletics team made the University Sport South Africa (USSA) national championship, held over the weekend in Sasolburg, one to remember.
Eight athletes earned gold medals, which is twice as many as last year. The total of 24 medals (including seven silver and nine bronze) is also double the number won in 2017.
They ended third, just one gold medal below the University of Johannesburg. The female athletes won the women’s competition and contributed to seven of the eight gold medals. The Kovsies dominated the 1 500m and 5 000m events in which they both claimed the first, second and third spots. They even grabbed the fourth and sixth position in the 5 000m in which Kesa Molotsane triumphed, with Tyler Beling second, and Marné Mentz third. Mentz and Beling swapped places in the 1 500m behind Ts’epang Sello.
Molotsane also won the 10 000m in a record time of 34:49.16 which is a massive three minutes and five seconds quicker than the previous record.
The 800m title also now belongs to Sello. She and Beling won a third medal (bronze) as members of the 4 x 400m relay women’s team.
The other gold medalist was Lara Orrock (3 000m steeplechase), Lynique Beneke (long jump), Esli Lamley (pole vault) and Rynardt van Rensburg (800m).
All five Kovsies who competed at the CAA Southern Region Youth & Junior Championships that took place over the weekend in Boksburg, won medals. They were Pakiso Mthembu (gold, 5 000m), Michaéla Wright (gold, long jump), Tyler (gold, 1 500m), Tsebo Matsoso (silver, 200m) and Orrock (silver, 3 000m steeplechase).
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.