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18 October 2021
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Story Dr Nitha Ramnath
Our podcast guest
Prof Jeandrew Brink holds the post of Associate Professor in the Department of Mathematics and Applied
Mathematics within the Faculty of Natural and Agricultural Sciences at the University of the Free State. Her areas of interest include an analytical interest in Einstein field equations, dynamical systems, algorithms for testing general relativity
and the no-hair theorems, black holes, and gravitational waves. Prof Brink registered for a Bachelor of Sciences in 1998, continued with honours studies in 1999, and completed her master’s degree in 2000. Prof Brink’s research is on various
theoretical aspects of testing general relativity. She is a member of the MeerKAT telescope team that times and detect pulsars. She is also working with members of the Laser Interferometer Gravitational-Wave Observatory (LIGO) to find ways of using
gravitational waves to test Einstein’s theory.
Prof Brink was involved in the compilation of a video of geodesic orbits around a Manko-Novikov singularity, selected to be part of a Starmus 3 concert, the proceeds of which went towards promoting the Stephen Hawking Medal for Science Communication and
to support the charitable activities of the Stephen Hawking Foundation. It could also be part of a subsequent documentary.
Listen to the podcast at
François van Schalkwyk and Keenan Carelse,
UFS alumni leading the university’s United
Kingdom Alumni Chapter, have put their voices together to produce and direct the podcast series. Intended to reconnect alumni with the university and their university experience, the podcasts will be featured on the first Monday of every month,
ending in November 2021. Our featured alumni share and reflect on their experiences at the UFS, how it has shaped their lives, and relate why their ongoing association with the UFS is still relevant and important. The podcasts are authentic
conversations – they provide an opportunity for the university to understand and learn about the experiences of its alumni and to celebrate the diversity and touchpoints that unite them.
For further information regarding the podcast series, or to propose other alumni guests, please email us at
alumnipodcast@ufs.ac.za
For all Voices from the Free State podcasts,
click here.
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.