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31 August 2020 | Story Xolisa Mnukwa
SRC election term extended

SRC elections 2020/21 were due to take place before the end of August 2020 as prescribed by the ISRC constitution. However, owing to the COVID-19 pandemic, and the consequent lockdown regulations and extension of the UFS 2020 academic year, the current SRC term will be extended until March 2021.

The decision to extend the term of the SRC was taken by the Rectorate following a recommendation made by the Division of Student Affairs (DSA), after consultation with
the ISRC. 

The consultation process with the ISRC produced three options:
  • Proceed with SRC elections in August 2020;
  • Extend the current SRC term to align with the extended 2020 academic year; or
  • Elect a Transitional Student Council (TSC) from September 2020 to March 2021.
In view of the above, and considering current conditions amid the coronavirus pandemic,
online SRC elections are scheduled for March 2021. 

This extension implies that the terms of all the sub-structures of the ISRC will be extended accordingly.

This communication serves as official notice to the Student Body about the extension of the
2019/2020 ISRC term and all its sub-structures as per the prescripts of the ISRC Constitution.

The DSA, with particular reference to the Student Governance Office (SGO), remains
committed to engaging with all parties of legitimate interest about matters arising from,
related to, and/or about SRC elections in all its permutations. 

Should you have any questions or comments, please feel free to contact the SGO:
Coordinator: Kamogelo Dithebe (DithebeKS@ufs.ac.za)
Faculty Coordinator: (MunzheleleD@ufs.ac.za)
Administrator: Rethabile Motseki (MotsekiR@ufs.ac.za)

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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