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14 June 2018 Photo Supplied
Next Chapter Green Ribbon campaign addresses mental health
Members of Next Chapter and UFS Student counselling are working together to address mental health issues.

Next Chapter, a student support group at the UFS presented the Green Ribbon campaign, pledging their support to students and providing them with assistance in coping with life events that stimulate stress and contribute negatively to their mental health. The team aims to break the stigma surrounding mental health care, and continually assist students with mental health-related issues that they struggle with daily.

The Green Ribbon represents mental health awareness, which is a pressing matter for students and is the type of support students need in a stressful university environment. The campaign focuses on teaching students how to cope with life events that stimulate stress, and contribute negatively to their mental health.
 
A discussion by Dr Ancel George: practising clinical psychologist and lecturer from the UFS Department of Psychology, and Dr Mellissa Barnaschone: Director of UFS Student Counselling, took place, where talks were prominent about creating an inclusive environment for UFS students.

The panel shared a few tips on how students should work towards managing stress, and motivated them for the main mid-year examinations.
 
The follow-up Exam Cram Workshop, presented by Nadia Cloete and Lize Wolmarans, that combined time and stress management, took place on 2 June 2018, and saw students receiving advice on how to approach various issues during the examination period.
 
Mental health awareness does not end with the campaign and Next Chapter’s slogan “Your story continues” encourages students to regularly wear and commemorate the green ribbon in support of continual mental healthcare.
 
Should you have any enquiries or input for the ongoing campaign, contact the Next Chapter team on ufsnextchapter@gmail.com, or further email Tshepang Mahlatsi, founder of Next Chapter on tshepangmahlatsi767@gmail.com

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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