Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
07 February 2018 Photo Adri Louw
KovsieFM programme manager joins SuperSport as field reporter
Sam Ludidi.

Sam Ludidi is no unfamiliar face on campus. He is currently busy with his second year of a BA Communication Science degree but started off as a BSocSc student five years ago. This KovsieFM programme manager recently joined the SuperSport team as a field reporter. He was selected from 70 candidates and recalls the phone call he received as the best he ever got. We checked in with him to see how he was enjoying the limelight.

It is difficult to choose between television and radio ... I think I prefer television. Then again, there’s a certain skill you need for radio because people don’t see you – that challenge intrigues me. But since I’m an expressive person, television allows me to express myself in full view of the audience.

Sport is my true passion, without a shadow of a doubt. I was born and raised in a sports-crazy house and always loved it – even watching the Proteas’ unfortunate loss to Australia in the Cricket World Cup when I was four. I’ve always loved cricket, but I just cannot keep myself away from rugby. Between the two sports, I’d probably lean towards rugby from an off-the-field perspective, and cricket if I’m on the field.

“You only have one chance
to make it work.”
—Sam Ludidi
Supersport Field Reporter

The best and worst thing about being a television presenter is that it is live. You only have one chance to make it work. When I get it right, I feel great, but on a difficult day, I am hard on myself. I’m still somewhat new to television, but the trick is to find out what makes me different from the rest. My character and charisma make me stand out.

I still can’t believe ... that I am doing my dream job, and it almost came out of nowhere. My incredible support structure from since before my TV presenter job still sticks with me. I learnt from my mother to glorify God with the work that I do, I know that He’s opened many doors which led to this and I cannot express just how blessed I am.

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