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12 October 2020
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Story Dr Cindé Greyling
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Photo Supplied
Exercise and nutrition can work wonders for your mental health – you don’t even have to ‘feel like’ or ‘enjoy’ moving around and eating well for it to work – it does its thing anyway.
Nowadays, people talk about mental health like it is the common cold – which is good! But do you know what it really means? Being mentally healthy does not only refer to the absence of a mental illness but includes your emotional and social well-being. One would almost want to add physical well-being too, since a healthy body does indeed support a healthy mind. However, since so many people consider themselves ‘mental health experts’, some myths have been sold as truths.
Myth #1 – You are doomed.
Nope. Never. You are never doomed. There is always help. Mental-health therapies range from self-help, talk therapy, medication, to hospitalisation in some cases. Somewhere on this spectrum of treatments, there will be something that works for you. But you must be willing to get the help and do the work. For starters, exercise and nutrition can work wonders – you do not even have to ‘feel like’ or ‘enjoy’ moving around and eating well for it to work – it does its thing anyway.
Myth #2 – It won’t affect you.
It may. Research suggests that one in five people may suffer from a mental illness at some point in their lives. Being well now does not mean that it will stay that way. Biological and environmental factors both impact your mental health. Hopefully not, but at some point, you may experience an event that affects your mental health.
To remain integrated in a community is always beneficial
for anyone suffering from a mental or physical condition.
Myth #3 – Someone struggling with mental health must be left alone.
Hardly! To remain integrated in a community is always beneficial for anyone suffering from a mental or physical condition. You do not need to fix them, but to remain a friend. Continue to invite them, even if they decline. Do not judge, and do not try to understand. Just stay around.
Go and be kind to yourself, and to those around you.
Mathematical methods used to detect and classify breast cancer masses
2016-08-10
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.