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08 April 2019 | Story Valentino Ndaba | Photo Valentino Ndaba
Andrew Lane
Mining the fourth industrial revolution way is the future says industry expert, Andrew Lane.

Innovation is imperative for the future of mining in South Africa. Industry expert, Andrew Lane proposes that leveraging on new information, mining technologies and energy knowhow, which are the hallmarks of the fourth industrial revolution, should set the scene for success.

Lane who is Africa Energy and Resource Leader at Deloitte, engaged students at a recent guest lecture hosted by the University of the Free State’s Business School on the Bloemfontein Campus. “The future is intelligent mining. It’s not just about technology; it’s about changing the way you do business,” he said.

Transforming traditional to trailblazing
“What gives you sustainable competitive advantage is the rate at which you innovate,” said Lane. Design paradigm shifts in the South African mining industry may have resulted in about 100 000 job losses during the past four years. However, mining companies stand to achieve significant gains through applying innovation.

Despite most of South Africa’s mines nearing the end of their lives, mining remains a large employer and investor attractor which ensures that the country holds a competitive advantage in the global economy. Lane is adamant that, “even though we have declined from 20% to 5% in terms of GDP contributions, mining remains a large contributor to export earnings”.

Reaching resource-rich regions
While some physical resources are inaccessible using current technology, “new mineral-processing technologies help tap into previously uneconomical mineral deposits”, according to Lane. In addition to the environment, 3D visualisation cameras can track employees and equipment in the bowels of the earth.

More mining, less loss
Integrating mining, energy, and information technology will ensure that companies reduce people, capital and energy intensity, while increasing mining intensity. The impossible can be achieved if technology is used well for developmental outcomes, employment, and improving standards of living.



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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