Geoff Dougherty

Measurement of retinal vascular tortuosity and its application to retinal pathologies

The tortuosity of retinal blood vessels is an important diagnostic indicator for a number of retinal pathologies. We applied robust quantitative tortuosity metrics, which are well suited to automated detection and measurement, to retinal fluorescein images of normal and diseased vessels exhibiting background diabetic retinopathy, retinitis pigmentosa and retinal vasculitis. We established the validity of the mean tortuosity (M) and the normalized root-mean-square tortuosity (K) by their strong correlation with the ranking of tortuosity by an expert panel of ophthalmologists. The low prevalences of the diseased conditions in the general population affect the classification process, and preclude the use of tortuosity for screening for all of these conditions simultaneously in the general population. Tortuosity may be useful as a screening test for retinitis alone, and may be useful for distinguishing diabetic retinopathy or vasculitis from normal in a discretionary (i.e. referred) population.

Automated Calculation of Water-equivalent Diameter (DW) Based on AAPM Task Group 220

The purpose of this study is to accurately and effectively automate the calculation of the water‐equivalent diameter (DW) from 3D CT images for estimating the size‐specific dose. DW is the metric that characterizes the patient size and attenuation. In this study, DW was calculated for standard CTDI phantoms and patient images. Two types of phantom were used, one representing the head with a diameter of 16 cm and the other representing the body with a diameter of 32 cm. Images of 63 patients were also taken, 32 who had undergone a CT head examination and 31 who had undergone a CT thorax examination. There are three main parts to our algorithm for automated DW calculation. The first part is to read 3D images and convert the CT data into Hounsfield units (HU). The second part is to find the contour of the phantoms or patients automatically. And the third part is to automate the calculation of DW based on the automated contouring for every slice (DW,all). The results of this study show that the automated calculation of DW and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of DW and the manual calculation are less than 0.5%. The results of this study also show that the estimating of DW,all using DW,n=1 (central slice along longitudinal axis) produces percentage differences of −0.92%±3.37% and 6.75%±1.92%, and estimating DW,all using DW,n=9 produces percentage differences of 0.23%±0.16% and 0.87%±0.36%, for thorax and head examinations, respectively. From this study, the percentage differences between normalized size‐specific dose estimate for every slice (nSSDEall) and nSSDEn=1 are 0.74%±2.82% and −4.35%±1.18% for thorax and head examinations, respectively; between nSSDEall and nSSDEn=9 are 0.00%±0.46% and −0.60%±0.24% for thorax and head examinations, respectively. PACS number(s): 87.57.Q‐, 87.57.uq‐The purpose of this study is to accurately and effectively automate the calculation of the water-equivalent diameter (DW) from 3D CT images for estimating the size-specific dose. DW is the metric that characterizes the patient size and attenuation. In this study, DW was calculated for standard CTDI phantoms and patient images. Two types of phantom were used, one representing the head with a diameter of 16 cm and the other representing the body with a diameter of 32 cm. Images of 63 patients were also taken, 32 who had undergone a CT head examination and 31 who had undergone a CT thorax examination. There are three main parts to our algorithm for automated DW calculation. The first part is to read 3D images and convert the CT data into Hounsfield units (HU). The second part is to find the contour of the phantoms or patients automatically. And the third part is to automate the calculation of DW based on the automated contouring for every slice (DW,all). The results of this study show that the automated calculation of DW and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of DW and the manual calculation are less than 0.5%. The results of this study also show that the estimating of DW,all using DW,n=1 (central slice along longitudinal axis) produces percentage differences of -0.92%±3.37% and 6.75%±1.92%, and estimating DW,all using DW,n=9 produces percentage differences of 0.23%±0.16% and 0.87%±0.36%, for thorax and head examinations, respectively. From this study, the percentage differences between normalized size-specific dose estimate for every slice (nSSDEall) and nSSDEn=1 are 0.74%±2.82% and -4.35%±1.18% for thorax and head examinations, respectively; between nSSDEall and nSSDEn=9 are 0.00%±0.46% and -0.60%±0.24% for thorax and head examinations, respectively. PACS number(s): 87.57.Q-, 87.57.uq.

Image analysis in medical imaging: recent advances in selected examples

Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments.

A fully automated calculation of size-specific dose estimates (SSDE) in thoracic and head CT examinations

The purpose of this study is to automatically calculate and then investigate the size- specific dose estimate (SSDE) in thoracic and head CT examinations undertaken using standard imaging protocols. The effective diameter (Deff ), the water equivalent diameter (Dw ), and the SSDE were calculated automatically from patient images. We investigated sixteen adult patients who underwent a CT head examination and thirty adult patients who underwent a CT thorax examination. Our results showed that the Dw value in the thoracic region was 4.5% lower than the value of Deff , while the Dw value in the head region was 8.6% higher than the value of Deff . The relationships between diameter (Deff and Dw ) and CTDIvol were distinctive. In the head region, decreasing the patient diameter resulted in a constant CTDIvol due to the tube current modulation (TCM) being off, while in the thoracic region decreasing the patient diameter resulted in a decrease in value of CTDIvol due to TCM being on. In the head region, decreasing the patient diameter resulted in an increase in the value of SSDE, while in the thoracic region decreasing the patient diameter resulted in a decrease in the value of SSDE.

Assessment of scoliosis by direct measurement of the curvature of the spine

We present two novel metrics for assessing scoliosis, in which the geometric centers of all the affected vertebrae in an antero-posterior (A-P) radiographic image are used. This is in contradistinction to the existing methods of using selected vertebrae, and determining either their endplates or the intersections of their diagonals, to define a scoliotic angle. Our first metric delivers a scoliotic angle, comparable to the Cobb and Ferguson angles. It measures the sum of the angles between the centers of the affected vertebrae, and avoids the need for an observer to decide on the extent of component curvatures. Our second metric calculates the normalized root-mean-square curvature of the smoothest path comprising piece-wise polynomial splines fitted to the geometric centers of the vertebrae. The smoothest path is useful in modeling the spinal curvature. Our metrics were compared to existing methods using radiographs from a group of twenty subjects with spinal curvatures of varying severity. Their values were strongly correlated with those of the scoliotic angles (r = 0.850 - 0.886), indicating that they are valid surrogates for measuring the severity of scoliosis. Our direct use of positional data removes the vagaries of determining variably shaped endplates, and circumvented the significant interand intra-observer errors of the Cobb and Ferguson methods. Although we applied our metrics to two-dimensional (2- D) data in this paper, they are equally applicable to three-dimensional (3-D) data. We anticipate that they will prove to be the basis for a reliable 3-D measurement and classification system.

Texture analysis using lacunarity and average local variance

Texture and spatial pattern are important attributes of images and their potential as features in image classification, for example to discriminate between normal and abnormal status in medical images, has long been recognized. In order to be clinically useful, a texture metric should be robust to changes in image acquisition and digitization. We compared four multi-scale texture metrics accessible in the spatial domain (lacunarity, average local variance (ALV), and two novel variations) in terms of ease of interpretation, sensitivity and computational cost. We analyzed a variety of patterns and textures, using simple synthetic images, standard texture images, and three-dimensional point distributions. ALV is invariant to brightness, but depends on image contrast; it detects the size of a pattern element as a large peak in the plot. Lacunarity shows the periodicity within an image. Normalizing lacunarity removes its dependence on image density, but not on image brightness and contrast, so that comparisons should always be made using histogram equalized images. We extended the treatment to grayscale images directly, which is not equivalent to a weighted sum of the normalized lacunarity of the bit-plane images. Different sampling schemes were introduced and compared in terms of resolution and computational tractability. The plots can be used directly as a texture signature, and parametric features can be extracted from monotonic lacunarity plots for classification purposes.

Profile of CT scan output dose in axial and helical modes using convolution

The profile of the CT scan output dose is crucial for establishing the patient dose profile. The purpose of this study is to investigate the profile of the CT scan output dose in both axial and helical modes using convolution. A single scan output dose profile (SSDP) in the center of a head phantom was measured using a solid-state detector. The multiple scan output dose profile (MSDP) in the axial mode was calculated using convolution between SSDP and delta function, whereas for the helical mode MSDP was calculated using convolution between SSDP and the rectangular function. MSDPs were calculated for a number of scans (5, 10, 15, 20 and 25). The multiple scan average dose (MSAD) for differing numbers of scans was compared to the value of CT dose index (CTDI). Finally, the edge values of MSDP for every scan number were compared to the corresponding MSAD values. MSDPs were successfully generated by using convolution between a SSDP and the appropriate function. We found that CTDI only accurately estimates MSAD when the number of scans was more than 10. We also found that the edge values of the profiles were 42% to 93% lower than that the corresponding MSADs.

Medical Imaging in the Diagnosis of Osteoporosis and Estimation of the Individual Bone Fracture Risk

Osteoporosis is a degenerative disease of the bone. In an advanced state, bone weakened by osteoporosis may fracture spontaneously with debilitating consequences. Beginning osteoporosis can be treated with exercise and calcium/vitamin D supplement, whereas osteoclast-inhibiting drugs are used in advanced stages. Choosing the proper treatment requires accurate diagnosis of the degree of osteoporosis. The most commonly used measurement of bone mineral content or bone mineral density provides a general orientation, but is insufficient as a predictor for load fractures or spontaneous fractures. There is wide agreement that the averaging nature of the density measurement does not take into account the microarchitectural deterioration, and imaging methods that provide a prediction of the load-bearing quality of the trabecular network are actively investigated. Studies have shown that X-ray projection images, computed tomography (CT) images, and magnetic resonance images (MRI) contain texture information that relates to the trabecular density and connectivity. In this chapter, image analysis methods are presented which allow to quantify the degree of microarchitectural deterioration of trabecular bone and have the potential to predict the load-bearing capability of bone.

Estimating and Comparing Classifiers

A variety of metrics have been used to estimate the performance of a classifier, and hence to compare different classifiers. Performance is specific to a particular problem and dataset, and there is no overall best classifier. Cross-validation and resampling methods need to be chosen carefully. Receiver operating characteristic (ROC) curves provide a convenient graphical method of comparing classifier performance, although there are a number of other statistical tests.

A SIMPLE METHOD FOR CALIBRATING PIXEL VALUES OF THE CT LOCALIZER RADIOGRAPH FOR CALCULATING WATER-EQUIVALENT DIAMETER AND SIZE-SPECIFIC DOSE ESTIMATE

The purpose of this study is to establish the relationship between the pixel value (I) of the CT localizer radiograph and water-equivalent thickness (tw) in a straightforward procedure. We used a body CTDI phantom, which was scanned in the AP and LAT projections. After transformation from the pixel values of the images to tw, water-equivalent diameter (Dw) and size-specific dose estimate were calculated on an anthropomorphic phantom and 30 patients retrospectively. We found a linear correlation between I and tw, with R2 ≥ 0.980. The Dw values based on the CT localizer radiograph were comparable to those calculated using axial images. The Dw difference for the anthropomorphic phantom between AP projection and axial images was 5.4 ± 4.2%, and between LAT projection and axial images was 6.7 ± 5.3%. The Dw differences for the patients between CT localizer radiograph and axial images was 2.3 ± 3.2%.