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16 January 2025 | Story Dr Cindé Greyling | Photo Supplied
Green Futures Hub
Prof Wayne Truter, who is leading the Green Futures Hub at the UFS, highlights that mining and agriculture are important yet competing industries in South Africa. The hub aims to find sustainable ways for them to coexist.

Our earth is very resilient, and a green future is possible, but we must make changes. At the forefront of this mission is the Green Futures Hub, spearheaded by Prof Wayne Truter at the UFS. Prof Truter holds a PhD in Integrated Agricultural and Environmental Sciences, with more than 25 years of experience. He is a leader in the field of forage, pasture, and land regeneration – particularly those impacted by mining. 

The Green Futures Hub is a virtual platform that bridges academic research and industry gaps, aiming to solve real-world challenges with scientific insights. It is designed to showcase and integrate the research happening across various disciplines at the University of the Free State (UFS), making it accessible to industry and communities alike. “People often lose faith in academic institutions, thinking that the research done there has no practical value,” Prof Truter notes. “The Green Futures Hub aims to change that by making scientific findings accessible and relevant to daily life.” 

This platform offers a unique opportunity for industries to connect with researchers working on solutions related to climate change, sustainable agriculture, or environmental rehabilitation. “Our hub is a space where industries can come to us with their challenges, and we can offer solutions based on research,” Prof Truter explains. “It’s about creating real impact.” 

Collaboration and integration are central to the Green Futures Hub’s approach. “Through interdisciplinary collaboration and a commitment to environmental stewardship, we want to develop solutions to the complex development challenges related to ecosystems, agroecosystems, water resources, biodiversity, infrastructure, and communities,” says Prof Truter. 

One of the hub’s projects that is close to Prof Truter’s heart, is the future coexistence of mining and agriculture. Mining and agriculture are two important industries in South Africa, often competing for land. However, the hub seeks to bridge this gap by exploring how these industries can coexist sustainably.  

“The future coexistence of mining and agriculture is critical,” says Prof Truter. “While mining often uses the land intensively, they have the responsibility and capability to rehabilitate it for agricultural use, ensuring that it is as productive – if not more – than it was before. Farmers and miners have much to gain from each other,” he explains. “By partnering with industries, we can help rehabilitate the land that has been mined, and in turn, farmers can harness and bring back the productivity to that land with the financial inputs of mining companies.” 

Prof Truter also emphasises the importance of science communication. “We need to do better at communicating the value of the research we’re doing. Many times, industries don’t understand the significance of what we’re working on because it’s not explained in a way that resonates with them. The hub ensures that research findings are accessible, understandable, and applicable to real-world issues.”  

The Green Futures Hub is more than just a research platform; it is a testament to the power of collaboration between academia and industry. “We’re not just conducting research,” Prof Truter concludes, “we’re developing solutions.” 

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.

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