Automatic mineral identification using BSE and EDS signals from an SEM

Dou, Mingxiao (2004). Automatic mineral identification using BSE and EDS signals from an SEM PhD Thesis, Julius Kruttschnitt Mineral Research Centre, University of Queensland.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
THE17711.pdf Full Text Click to show the corresponding preview/stream application/pdf 147.14MB 11
Author Dou, Mingxiao
Thesis Title Automatic mineral identification using BSE and EDS signals from an SEM
School, Centre or Institute Julius Kruttschnitt Mineral Research Centre
Institution University of Queensland
Publication date 2004
Thesis type PhD Thesis
Supervisor Dr. Ying Gu
Dr. Stephen L. Gay
Total pages 298
Collection year 2004
Language eng
Subjects L
290702 Mineral Processing
640300 First Stage Treatment of Ores and Minerals
640205 Other non-ferrous ores (e.g. copper, zinc)
Formatted abstract The scanning electron microscope has been used for determining mineral composition and gathering geometric data of particles for mineral liberation analysis for decades. With the increasing power of computers, it is possible to further extend the use of scanning electron microscopy to achieve more accurate and faster mineral liberation analysis in real time. In such an effort, it is necessary to develop algorithms to improve back-scattered electron image segmentation to generate more accurate mineral phase maps. X-ray spectrum analysis using least square fit, which is widely used in applications in many fields, is relatively mature and offers better accuracy in terms of X-ray energy and intensity. Adapting the X-ray spectrum analysis techniques for a scanning electron microscope and linking the X-ray spectrum analysis to mineral identification become another necessary part.

Algorithms are developed for image segmentation and mineral identification respectively. The proposed image segmentation algorithm measures the "significance" of the grey-level uniformity of a region and evaluates the "effective grey-level difference" between two adjacent regions with a scale control built in. Then it uses a multi-level iteration of region-merging to overcome the multi-scale problem. A pattern matching algorithm is proposed to match the line series obtained by least square fit from the X-ray spectrum of unknown mineral, to the standard line series of minerals stored in the mineral database. It traverses all possible matches from the sample line series to standard line series in the database for the best match. A null line is introduced to cope with the problem that occurs when line series contain false lines and two series have different number of lines.

The experiments of both algorithms give satisfactory results in most cases that are considered "normal" in actual measurement. At very high magnification, the limitations of the segmentation algorithm become obvious, such as crack and shadow extending unexpectedly and scalability getting worse. The pattern matching algorithm for X-ray line series match fails to distinguish haemetite and magnetite, which have close elemental composition. The difference in the iron content is about 2%. It also fails to distinguish diopside and Cr-diopside. The chromium content in Cr-diopside is 0.34%, which is too low for the characteristic X-ray of Cr to be detectable in the spectrum. The chromium content does not have any detectable impact on the total elemental composition of Cr-diopside as well. With 52 minerals tested, except for the above two mineral pairs, all other minerals tested are reliably distinguished.

Chapter one gives an introduction on mineral identification using an SEM and literature reviews on both image segmentation and X-ray spectrum analysis applications. The problems in developing a new segmentation algorithm and applying X-ray spectrum analysis to mineral identification using a SEM are also addressed in chapter one. Chapter two discusses the algorithm development for BSE image segmentation. The experiment of the proposed segmentation algorithm is discussed in chapter three. Chapter four discusses the methodologies for applying X-ray spectrum analysis to SEM. Problems specific to mineral identification using an SEM, are also addressed. A pattern match algorithm is proposed to match standard line series and sample line series. Chapter five discusses the experiment of the pattern match algorithm. Chapter six summarises the experiment results and discusses conclusions.

Keyword Mineralogy, Determinative
Scanning electron microscopes
X-ray spectroscopy

Document type: Thesis
Collection: UQ Theses (RHD) - UQ staff and students only
Citation counts: Google Scholar Search Google Scholar
Access Statistics: 540 Abstract Views, 11 File Downloads  -  Detailed Statistics
Created: Fri, 24 Aug 2007, 18:28:52 EST