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05 November 2024 | Story Jacky Tshokwe | Photo Supplied
BUAN Delegates
Botswana University of Agriculture and Natural Resources (BUAN) delegates that recently visited the University of the Free State to solidify a collaboration.

During an inspiring journey, a delegation from the University of the Free State (UFS) recently visited the Botswana University of Agriculture and Natural Resources (BUAN) with an ambitious goal: to solidify a collaboration that was sparked during the visit of BUAN’s Vice-Chancellor to the UFS and subsequent discussions in Namibia. The atmosphere was one of shared purpose and excitement as the UFS representatives, led by the Dean of the Faculty of Natural and Agricultural Sciences, the Vice-Dean: Agriculture, and the Vice-Dean: Postgraduate and Research, embarked on this significant academic endeavour.

This visit was not just a formal gesture, it was a step towards tangible, mutual benefits for students and staff of both institutions. The discussions between the UFS and the BUAN leadership, which centred around possibilities for student and staff exchanges and shared access to specialised equipment, pointed to the potential of creating a dynamic bridge between South African and Botswana academia. This partnership envisions collaborative supervision of postgraduate students, creating opportunities for intellectual growth that transcends borders. The two universities also explored joint funding applications and research avenues, with particular interest in BUAN’s innovative Meat Institute and AgroVolts solar panel project. Seeing the BUAN’s progress in renewable energy left the UFS team particularly impressed, reflecting the possibilities for sustainable development and resource-sharing that a partnership could yield.

During the discussions, the UFS delegation had a pivotal meeting with the BUAN’s Acting Deputy Vice-Chancellor: Academic and Research, Prof Samodimo Ngwako, who had previously visited the UFS. His familiarity with the UFS’ resources and vision made him an invaluable advocate for bridging the two institutions, highlighting how their strengths could complement each other. Prof Ngwako’s insights helped BUAN staff visualise the meaningful exchange of expertise and resources that could benefit both student bodies and contribute to third-stream income generation.

With the way forward clear, both the UFS and BUAN teams agreed on ‘quick steps’ to launch the collaboration – the swift signing of a Memorandum of Understanding (MoU), followed by the first exchange of students and staff, and the launch of co-supervised research projects. While specific timelines and milestones are to be confirmed post-MoU, both teams are keen on joint funding applications, especially in areas relevant to agricultural and natural resources both within Africa and beyond. This partnership, once formalised, is expected to solidify both universities as leading research hubs in agriculture and natural resources, advancing each institution’s standing on the continent.

Reflecting on the visit, the UFS delegation felt a deep sense of optimism. The collaboration between the UFS and the BUAN aligns seamlessly with the UFS’ broader vision for strengthening ties with African universities, showcasing a forward-thinking approach to partnerships. As the journey towards meaningful collaboration progresses, the shared enthusiasm witnessed at the BUAN serves as a hopeful reminder that academia – when united by common goals – can drive impactful change for students, faculty, and communities on both sides of the border.

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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