Latest News Archive

Please select Category, Year, and then Month to display items
Categories
UFS News Media Release Research
Years
2017 2018 2019 2020 2021 2022 2023 2024
Months
January February March April May June July August September October November December
Previous Archive
29 January 2020 | Story Xolisa Mnukwa
Kovie Act
For more information on the 2020 Kovsie ACT programme and the upcoming events, visit the UFS Kovsie ACT website page, email: jool@ufs.ac.za or call: +27 51 401 2718 or visit Kovsie ACT on Facebook and Twitter.
The University of the Free State (UFS) is preparing for an exciting 2020 KovsieAct programme, with Amapiano superstar Kabza de Small, deep house music pioneers Black Motion, and musical sensations Spoegwolf and Early B poised to entertain students, staff, and the public at a Kovsie ACT music festival on 1 February 2020. 

Something new to the programme this year is the Kovsie ACT eco-vehicle parade through the streets of Bloemfontein. This parade replaced the old RAG float building and procession.

This is done with the intention to foster a close relationship with the broader Bloemfontein community. The parade on 1 February 2020 is also a celebration by first-year students of their entry into the UFS campus community.

The parade will be followed by an eco-vehicle race taking place on the UFS Bloemfontein Campus. Five teams will compete in categories including an Endurance race, Slalom course, Obstacle course, and Formula1-inspired race. Karen Scheepers, 
UFS Assistant Director for Student Life, says Kovsie ACT is a great opportunity for students to learn about sustainable environmental development through exciting community-building activities. “It’s an opportunity for them to learn new skills and build valuable relationships.”

“Skills developed through the programme include students learning to listen and communicate better; they also acquire time-management and relationship-building skills. Kovsie ACT also propels them to persevere and practise responsibility and pride in the activities they participate in throughout the programme, which sees them personify the term ‘only a Kovsie knows the feeling’,” Scheepers explained. 

Dr WP Wahl, Director: Student Life in the Department of Student Affairs, says the UFS has already initiated the next phase of the eco-vehicle project.  

“The Department of Student Affairs, in partnership with merSETA and the Department of Engineering Sciences (Faculty of Natural and Agricultural Sciences), is developing six skills programmes that will significantly enhance the developmental impact of this programme for participating students.  To this effect, a team of engineers and instructional designers are working with the UFS to ensure that the necessary competencies are embedded in these skills programmes, which will help graduates compete on a global scale.  Students will be able to apply to become part of this high-tech phase of the eco-vehicle project during April 2020.” 

Kovsie ACT programme

09:00 – Kovsie Act Parade departing from the UFS Furstenburg Gate. Short parade through Mangaung: Nelson Mandela Drive – Zastron Street – 2nd Avenue – Kellner Street and return via Nelson Mandela Drive to the UFS

11:00 - Parade arrives back at UFS Furstenburg Gate

11:30–14:00: Eco-vehicle race at Mokete Square (previously known as the Red Square) on the Bloemfontein Campus

 16:00–till late: Kovsie ACT Music Festival at Bloemfontein Campus Rag Farm 
For more information on the above-mentioned events, visit the UFS Kovsie ACT page, email: jool@ufs.ac.za or call: +27 51 401 2718 or visit Kovsie ACT on Facebook and Twitter.

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