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The University of the Free State (UFS) wishes to confirm that the following financial concessions have been made to enable students to register for the 2024 academic year:
- Students with confirmed NSFAS funding:
- Students with a confirmed National Student Financial Aid Scheme (NSFAS) funding allocation for 2024 with a debt of R20 000 and less may register fully without making any payments.
- First-time entering students (FTENS) with a confirmed NSFAS funding allocation for 2024 may register fully without any payments.
- Students with a confirmed NSFAS funding allocation for 2024 with a debt of R30 000 and less may register provisionally and pay the required fees* for provisional registration.
- South African self-paying (NON-NSFAS) students:
- SA students with a debt of up to R500 may register fully without making any payments.
- SA students with a debt of up to R30 000 may register provisionally and pay the required fees* for provisional registration.
- FTENS not on UFS funded list:
- Students who are not on the funded list but report that they have been approved on their portal must contact our
Financial Aid Offices urgently so that the university can escalate to NSFAS.
The university will have continuous engagement with the National Financial Aid Scheme (NSFAS) to resolve outstanding matters. The university’s Financial Working Group (FWG) will meet regularly to determine how it can best assist students taking into consideration the financial constraints of the university.
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.