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06 November 2020
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Story Rulanzen Martin
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Photo Supplied
Dr Tronél Hellberg, UFS alumna, completed her PhD in Music from the OSM in 2018.
The COVID-19 pandemic and subsequent lockdown has posed many challenges. Not only has it distrupted our normal way of life it but has created a ‘new normal.’ Even in these trying times, alumni from the University of the Free State (UFS) have adjusted to the new normal by going above and beyond to make it as normal as possible.
One of these is Dr Tronél Hellberg, an alumna from the
Odeion School of Music at the UFS, who has supported Grade 12 learners by presenting free online prescribed music theory classes. The classes are beneficial for learners following the CAPS or IEB curriculum. “I trust the online videos will assist learners and teachers to get through this challenging Grade 12 year,” says Dr Hellberg. She has
recorded more than 38 live videos on her
G-Sential Theory of Music Facebook page.
The recordings are accessible to Grade 12 learners and their teachers at no cost. Dr Hellberg established the
G-Sential Theory of Music in 2007 and has since published 20 theory of music books.
Apart from assisting in teaching, one of her main objectives is to reach less fortunate learners who do not have access to music teachers. “Grade 12 music literacy requires an accumulative understanding of theory of music,” she says. With her initiative she also aims to “fill any gaps” to solidify knowledge and information which might still be unclear.
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.