Duncan Bell

3-D printout of a DICOM file to aid surgical planning in a 6 year old patient with a large scapular osteochondroma complicating congenital diaphyseal aclasia

A 6 year old girl presented with a large osteochondroma arising from the scapula. Radiographs, CT and MRI were performed to assess the lesion and to determine whether the lesion could be safely resected. A model of the scapula was created by post-processing the DICOM file and using a 3-D printer. The CT images were segmented and the images were then manually edited using a graphics tablet, and then an STL-file was generated and a 3-D plaster model printed. The model allowed better anatomical understanding of the lesion and helped plan surgical management.

Colour and Texture Based Gastrointestinal Tissue Discrimination

Wireless capsule endoscopy is a colour imaging technology that enables close examination of the interior of the entire small intestine. The wireless capsule endoscope (WCE) operates for ~ 8 hours and captures ~ 40,000 useful images. The images are viewed by a clinician as a video sequence, generally taking over an hour to analyse. In this paper we present a method of automatically discriminating stomach and intestine tissue which can significantly speed-up one key part of the video analysis time, namely the process of locating the Pylorus - the valve between the stomach and the intestine. We divide the WCE image into 28 sub-regions and process only those regions where tissue is clearly visible. We create a feature vector using colour and texture information. The colour features are derived from hue saturation chromaticity histograms of the useful regions, compressed using a hybrid transform, incorporating the discrete cosine transform (DCT) and principal component analysis (PCA). The texture features are derived by singular value decomposition of the same tissue regions. After training the support vector classifier, we apply a discriminator algorithm, which scans the video with an increasing step and builds up a classification result sequence. By minimizing the number of misclassifications within this sequence, we predict the most probable position of the Pylorus. We present experimental results that demonstrate the effectiveness of this method

Imaging live bee brains using minimally-invasive diagnostic radioentomology.

Abstract The sensitivity of the honey bee, Apis mellifera L. (Hymeonoptera: Apidae), brain volume and density to behavior (plasticity) makes it a great model for exploring the interactions between experience, behavior, and brain structure. Plasticity in the adult bee brain has been demonstrated in previous experiments. This experiment was conducted to identify the potentials and limitations of MicroCT (micro computed tomograpy) scanning “live” bees as a more comprehensive, non-invasive method for brain morphology and physiology. Bench-top and synchrotron MicroCT were used to scan live bees. For improved tissue differentiation, bees were fed and injected with radiographic contrast. Images of optic lobes, ocelli, antennal lobes, and mushroom bodies were visualized in 2D and 3D rendering modes. Scanning of live bees (for the first time) enabled minimally-invasive imaging of physiological processes such as passage of contrast from gut to haemolymph, and preliminary brain perfusion studies. The use of microCT scanning for studying insects (collectively termed ‘diagnostic radioentomology’, or DR) is increasing. Our results indicate that it is feasible to observe plasticity of the honey bee brain in vivo using diagnostic radioentomology, and that progressive, real-time observations of these changes can be followed in individual live bees. Limitations of live bee scanning, such as movement errors and poor tissue differentiation, were identified; however, there is great potential for in-vivo, non-invasive diagnostic radioentomology imaging of the honey bee for brain morphology and physiology.

3-D visualisation, printing, and volume determination of the tracheal respiratory system in the adult desert locust, Schistocerca gregaria

Here, we describe a single micro‐CT scan with a spatial resolution of 10 μm of a 10‐day‐old adult male Schistocerca gregaria (Forskål) (Orthoptera: Acrididae) and we compare our tracheal volume (VT) determination with published work on the subject. We also illustrate the feasibility of performing non‐invasive ‘virtual dissection’ on insects after performing micro‐CT. These post‐processing steps can be performed using free downloadable 3‐D software. Finally, the values of producing stereo‐lithography (STL) files that can be viewed or used to print out 3‐D models as teaching aids are discussed.

Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

Otsu’s criteria is a popular image segmentation approach that selects a threshold to maximise the inter-class variance of the distribution of intensity levels in the image. The algorithm finds the optimum threshold by performing an exhaustive search, but this is time-consuming, particularly for medical images employing 16-bit quantisation. This paper investigates particle swarm optimisation (PSO), Darwinian PSO and Fractional Order Darwinian PSO to speed up the algorithm. We evaluate the algorithms in medical imaging applications concerned with volume reconstruction, with a particular focus on addressing artefacts due to immobilisation masks, commonly worn by patients undergoing radiotherapy treatment for head-and-neck cancer. We find that the Fractional-Order Darwinian PSO algorithm outperforms other PSO algorithms in terms of accuracy, stability and speed which makes it the favourite choice when the accuracy and time-of-execution are a concern.