Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
09 October 2020 Photo Supplied
Kgalalelo Motlhabane
A Master of Commerce degree with specialisation in Industrial Psychology will be conferred on Kgalalelo Motlhabane, a graduate and funding officer at the Postgraduate School at UFS on Friday 9 October 2020 in a virtual graduation.

One of the success stories of this year’s virtual graduation is Kgalalelo Motlhabane, a graduate and funding officer at the Postgraduate School, University of the Free State (UFS), who was presented with an award from the National Research Foundation as the best designated authority for 2019.

Motlhabane, who lost her mother when she was in high school, didn’t just have to overcome poverty on her way to her first qualification, but also the fear of ending up on the street. On Friday 9 October she received a Master of Commerce degree with specialisation in Industrial Psychology.

A lot of challenges

“I had a lot of challenges during my first-year undergraduate studies. I could not afford textbooks and stationery. I spent most of my time in the library studying. More often I would be without food and proper clothes to wear.

“I would get warnings from my landlord when the rent was late. Sometimes I would find my room locked and my personal stuff removed. Not being able to pay outstanding fees restricted me from receiving my final results at the end of semesters and exacerbated the situation,” says Motlhabane.

Regardless of all these challenges, she was determined to not give up and return home to the impoverished community of Itireleng village near Pampierstad in the Northern Cape.

Overcoming the challenges

It was actually the distress, poverty and the difficult situation at home, together with her daily struggles that kept her focused. Through hard work, she managed to receive funding from NSFAS for her second and third years and despite the hardships, she obtained her BSocSci degree with distinction at the end of 2012.

“I was then selected to enroll for an Honours degree in Industrial Psychology in 2013. Without any funding prospects for my studies, I wrote to the former university rector Prof Jonathan Jansen, seeking financial assistance.

“Prof Jansen was very impressed with my exceptional academic performance and offered to pay my fees. He also offered me a position to work as a student assistant. I worked for three hours every day before going to the library to do my assignments and prepare for classes, presentations, tests and exams. I completed my Honours degree in 2013.”

In 2014, Motlhabane was employed as an intern at the UFS Human Resources Department and also enrolled for a postgraduate diploma in gender studies, which she obtained at the end of that year. The following year she joined the postgraduate school and started with her Master’s degree in late 2016.

Obtaining a Master’s degree

This was no easy task as she was employed full time which left her with little time to work thoroughly on her thesis, her limited knowledge about research, work pressures and demands, rejections, lack of support, discouragements, accidents and the inability to cope also played a role.  

“I would like to thank God for the strength he gave me to cope throughout my journey, my family for their prayers and continued support, as well as Prof Jonathan Jansen, Prof Petrus Nel and Prof Ebben van Zyl for their kind support and contribution towards my studies. Indeed, the future belongs to those who believe in the beauty of their dreams,” says Motlhabane.

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