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13 September 2019 | Story Rulanzen Martin | Photo Sonia Small (Kaleidoscope Studios)
#UFSRun4MentalHealth
The #UFSRun4MentalHealth is an initiative to create awareness around mental health.

Bringing hope to the millions of South Africans suffering from mental illness, is the message the #UFSRun4MentalHealth team wants to resonate when they take on the 1 075 km distance between Bloemfontein and Stellenbosch.  

On Friday 20 September 2019, three teams of enthusiastic runners from the Faculty of Health Sciences and Organisational Development and Employee Wellness at the University of the Free State (UFS) will embark on the first UFS mental-health awareness run to Stellenbosch. Each runner will complete 9 km each day. “We will be passing on the baton of hope. There is hope, and no one is alone,” says Burneline Kaars, Head of Employee Wellness at the UFS. 

The #UFSRun4MentalHealth run will end on the campus of Stellenbosch University (SU) on 25 September 2019, with the symbolic handover of the baton of hope to a representative of the SU management. 

Team Blue

Team Blue. From the left: Jo-mari Horn, Patrick Kaars, Burneline Kaars, Riaan Bezuidenhout, George Dumisi, and Eugene Petrus.
(absent: Hendrik Blom)

#UFSRun4MentalHealth part of larger project

“This initiative is our effort to mitigate the impact of inactivity experienced by our students and staff on their productivity and mental health. The purpose is to raise awareness and motivate people to get active,” says Burneline. Through this effort, the UFS is demonstrating care for student and staff well-being. 

“Well-being is not only the responsibility of the organisation or university, but the responsibility of all of us,” says Prof Francis Petersen, Rector and Vice-Chancellor. “This initiative also demonstrates care – to look after one another, to take care of one another –from the organisation to our people, but also among ourselves.” 

Prof Petersen points out that the #UFSRun4MentalHealth forms part of a larger UFS project called ‘Project Caring’. He is also hopeful that the team’s effort to change the perception of mental health will encourage discussion and openness in the towns they will visit on their way to Stellenbosch.

Team Red. From the left: Arina Meyer, Nico Piedt, Brenda Coetzee, Justin Coetzee, Elna de Waal, De Wet Dimo, and Tertia de Bruin.

Team Red. From the left: Arina Meyer, Nico Piedt, Brenda Coetzee, Justin Coetzee, Elna de Waal, De Wet Dimo, and Tertia
de Bruin.

Putting care into action

“With this run to Stellenbosch, we are putting care into action,” says Susan van Jaarsveld, Senior Director, Human Resources. 
According to the South African Depression and Anxiety Group, 16% or about 9 million of South Africa’s adult population suffer from a mental disorder. “With this increased awareness, we hope that people will share their mental-health diagnoses and that this campaign will help to reduce the stigma surrounding mental health.”  

The #UFSRun4MentalHealth also links to the mission of the UFS Department of Human Resources to create an environment not only for high performance, but for optimal performance.

The sponsors of this initiative are BestMed, Standard Bank, Shell, Annique Health and Beauty, Xerox, Bidvest Car Rental, Media24, Kloppers, New Balance, Clover, Futurelife, Mylan, Pharma Dynamics, and the SA Society of Psychiatrists

Team White. From the left: Thys Pretorius, Lynette van der Merwe, Leon Engelbrecht, Arina Engelbrecht, Teboho Rampheteng, Belinda Putter, and Lucas Swart.

Team White. From the left: Thys Pretorius, Lynette van der Merwe, Leon Engelbrecht, Arina Engelbrecht, Teboho Rampheteng,
Belinda Putter, and Lucas Swart.

 


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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