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25 April 2019 | Story Mamosa Makaya
Since 2016, the University of the Free State Center for Universal Access and Disability Support (CUADS) has received a grant from First National Bank worth R2 498 000, which supports tertiary bursaries for students with disabilities. Bursary holders are funded through CUADS, as the administrator of the bursaries.
  
These are students enrolled for various academic programmes who require academic assistance and/or assistive devices such as electronic handheld magnifiers, laptops, and hearing aids. The FNB grant also covers tuition, accommodation, study material and books, and meals.  The success of the grant is already evident, with one of the recipients having graduated with a Bachelor of Arts degree in December 2018. A second student was capped at the April 2019 graduations with a BSc Honours in Quantity Surveying.
 
Supporting the principles of the ITP

The UFS received the grant from FNB in instalments, starting in the 2016 academic year to date, supporting the needs of 40 disabled students. This grant and the work of CUADS speaks to and supports the principles of the Integrated Transformation Plan (ITP), namely inclusivity, transformation, and diversity. The vision of the Universal Access work stream is to enable the UFS to create an environment where students with disabilities can experience all aspects of student life equal to their non-disabled peers. The ITP provides for the recognition of the rights of people with disabilities as an important lesson in social justice and an opportunity to reinforce university values.

The successful administration of the grant to benefit past and present students is a ‘feather in the cap’ of CUADS, and is a shining example of the impact of public private investment and the endless possibilities that open up when there is a commitment to developing future leaders in academic spaces, allowing them to thrive by creating a learning environment that is welcoming and empowering. 



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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