Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
16 September 2020 | Story Xolisa Mnukwa | Photo Supplied
UFS Division of Student Affairs plans to extend their annual research colloquium to involve other universities, establishing the university as a pioneering institution of higher education and learning.

The annual University of the Free State (UFS) Division of Student Affairs Research (DSA) Colloquium aims to promote a culture of research embedded in data-driven and evidence- based practices in the field of Student Affairs. The purpose of the annual colloquium is to create a safe, enjoyable space for staff members to share their experiences, knowledge, research and practices.

The 2020 Student Affairs Research Colloquium was the first virtual Research Colloquium held by the university. As noted by DSA Researcher Ruben Langenhoven, this year’s theme Virtually Human: Connecting Meaningfully in a Digital World was inspired by the challenging times we live in, and thus commemorated the resilience and adaptability UFS Student Affairs practitioners, academic staff and students. 

As most of the projects and programmes in the DSA were negatively impacted, the Colloquium was threatened by a lack of “hard data” emanating from the 2020 academic year. The division consequently decided to reframe the colloquium by profiling distinct human voices that focused on qualitative experiences. As such, this Colloquium comprised of numerous sections where the emphasis was placed on shared experiences and shared understanding where UFS staff members and students discussed the challenges they faced in the last six months.

DSA staff engaged one another with staff and student-centered lived experiences, and professional staff development sessions that visited the impact of technology on their psychological well-being and how to improve their relationship with technology in light of the COVID-19 pandemic. Also forming part of the programme that will inform the future of the division going forward, reflected DSA success-story presentations of the past year. 

The colloquium proved as beneficial for the DSA and the entire institution in its pursuit of a research-based working approach within the Student Affairs discipline. 

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.

We use cookies to make interactions with our websites and services easy and meaningful. To better understand how they are used, read more about the UFS cookie policy. By continuing to use this site you are giving us your consent to do this.

Accept