Latest News Archive

Please select Category, Year, and then Month to display items
Years
2019 2020 2021 2024
Previous Archive
29 July 2019 | Story Leonie Bolleurs
Dr Martin Clark
Dr Martin Clark, the founder of the MAGIC (Multi-purpose Aerial Geological Image Classification) initiative. MAGIC can obtain geological and structural information that is critical for making informed decisions in exploration and mineral extraction processes.

Mining has historically been described as a boom-and-bust industry, where fluctuations in mineral prices could result in extreme success or bankruptcy. Successful mining companies closely monitor assets/expenditures, risks, and other parameters associated with their business to best ensure their longevity. In most mineral industries, there are a few competitors that dominate the delivery of a mineral resource. As a result, technological development, along with other factors, are critical to ensure that these companies’ business remains viable and protected.

This is according to post-doctoral fellow in the Department of Geology, Dr Martin Clark.

Drone technology: better, faster, safer

He says technological development in mining generally translates to how a company can extract a resource from the ground better, faster, and safer. 

Dr Clark believes the rapid development of drone technology represents a shift in the toolbox that mining companies can employ.

“Drones can collect a great deal of data randomly over vast or small areas within hours, historically accomplished by mapping campaigns which can last months to years. Drones can also collect data in areas which are difficult and dangerous for humans to get to. These include cliff faces or rock walls that are difficult and dangerous to get close to, as well as stretches of land where dense vegetation, inaccessible terrain, and even atmospheric dangers become factors which reduce or modify the scope of exploration work,” he said. 

Expanding application of drones

Dr Clark’s work specifically focuses on expanding the applications for which drones are used. “I assess what and how good the imaging capabilities of drones are, use the imagery to generate 3-D models to drive scientific observation, and yield results which can help companies to extract resources. This initiative is called MAGIC (Multi-purpose Aerial Geological Image Classification),” he said. 



“MAGIC aims to collect geological and structural information that is critical for making informed decisions in exploration and mineral extraction processes,” he added.

Dr Clark is not only the founder of MAGIC; he also drives multiple aspects of the initiative including education, research, and business development. 

In 2013, when he was busy with his doctorate, there was already a spark of interest in using drones to address geological questions. At that time, Dr Clark was working with remotely sensed high-resolution LiDAR imagery to better understand geological structures at the Sudbury Mining Camp in Canada. The interest became a reality in 2018, when he applied this initiative during his post-doctoral fellowship at the UFS.

Now and the future

“At present, there are no direct mining projects underway, but projects are expected to begin in 2020. Drone operation and image-analysis techniques are currently being refined for industry,” he said. 

Besides his work with drones, Dr Clark also work in the fields of structural geology, remote sensing, and geospatial data analysis.  

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.

We use cookies to make interactions with our websites and services easy and meaningful. To better understand how they are used, read more about the UFS cookie policy. By continuing to use this site you are giving us your consent to do this.

Accept