Enhanced algorithms for lesion detection and recognition in ultrasound breast images

Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance. [Continues.

Individualised grid-enabled mammographic training system

The PERFORMS self-assessment scheme measures individuals skills in identifying key mammographic features on sets of known cases. One aspect of this is that it allows radiologists’ skills to be trained, based on their data from this scheme. Consequently, a new strategy is introduced to provide revision training based on mammographic features that the radiologist has had difficulty with in these sets. To do this requires a lot of random cases to provide dynamic, unique, and up-to-date training modules for each individual. We propose GIMI (Generic Infrastructure in Medical Informatics) middleware as the solution to harvest cases from distributed grid servers. The GIMI middleware enables existing and legacy data to support healthcare delivery, research, and training. It is technology-agnostic, data-agnostic, and has a security policy. The trainee examines each case, indicating the location of regions of interest, and completes an evaluation form, to determine mammographic feature labelling, diagnosis, and decisions. For feedback, the trainee can choose to have immediate feedback after examining each case or batch feedback after examining a number of cases. All the trainees’ result are recorded in a database which also contains their trainee profile. A full report can be prepared for the trainee after they have completed their training. This project demonstrates the practicality of a grid-based individualised training strategy and the efficacy in generating dynamic training modules within the coverage/outreach of the GIMI middleware. The advantages and limitations of the approach are discussed together with future plans

Grid-enabled mammographic auditing and training system

Effective use of new technologies to support healthcare initiatives is important and current research is moving towards implementing secure grid-enabled healthcare provision. In the UK, a large-scale collaborative research project (GIMI: Generic Infrastructures for Medical Informatics), which is concerned with the development of a secure IT infrastructure to support very widespread medical research across the country, is underway. In the UK, there are some 109 breast screening centers and a growing number of individuals (circa 650) nationally performing approximately 1.5 million screening examinations per year. At the same, there is a serious, and ongoing, national workforce issue in screening which has seen a loss of consultant mammographers and a growth in specially trained technologists and other nonradiologists. Thus there is a need to offer effective and efficient mammographic training so as to maintain high levels of screening skills. Consequently, a grid based system has been proposed which has the benefit of offering very large volumes of training cases that the mammographers can access anytime and anywhere. A database, spread geographically across three university systems, of screening cases is used as a test set of known cases. The GIMI mammography training system first audits these cases to ensure that they are appropriately described and annotated. Subsequently, the cases are utilized for training in a grid-based system which has been developed. This paper briefly reviews the background to the project and then details the ongoing research. In conclusion, we discuss the contributions, limitations, and future plans of such a grid based approach

A comparative study in ultrasound breast imaging classification

American College of Radiology introduces a standard in classification, the breast imaging reporting and data system (BIRADS), standardize the reporting of ultrasound findings, clarify its interpretation, and facilitate communication between clinicians. The effective use of new technologies to support healthcare initiatives is important and current research is moving towards implementing computer tools in the diagnostics process. Initially a detailed study was carried out to evaluate the performance of two commonly used appearance based classification algorithms, based on the use of Principal Component Analysis (PCA), and two dimensional linear discriminant analysis (2D-LDA). The study showed that these two appearance based classification approaches are not capable of handling the classification of ultrasound breast image lesions. Therefore further investigations in the use of a popular feature based classifier – Support Vector Machine (SVM) was conducted. A pre-processing step before feature based classification is feature extraction, which involve shape, texture and edge descriptors for the Region of Interest (ROI). The input dataset to SVM classification is from a fully automated ROI detection. We achieve the success rate of 0.550 in PCA, 0.500 in LDA, and 0.931 in SVM. The best combination of features in SVM classification is to combine the shape, texture and edge descriptors, with sensitivity 0.840 and specificity 0.968. This paper briefly reviews the background to the project and then details the ongoing research. In conclusion, we discuss the contributions, limitations, and future plans of our work

Fully automatic lesion boundary detection in ultrasound breast images

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to preprocess the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing stateof- the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean technique proposed by Yap et. al., in proving the proposed method’s robustness and accuracy

Human activity recognition for physical rehabilitation

The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance; we undertook an incremental increase in the dataset size.We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set. © 2013 IEEE

Human Activity Recognition for Physical Rehabilitation

The recognition of human activity is a challenging topic for machine learning. We present an analysis of Support Vector Machines (SVM) and Random Forests (RF) in their ability to accurately classify Kinect kinematic activities. Twenty participants were captured using the Microsoft Kinect performing ten physical rehabilitation activities. We extracted the kinematic location, velocity and energy of the skeletal joints at each frame of the activity to form a feature vector. Principle Component Analysis (PCA) was applied as a pre-processing step to reduce dimensionality and identify significant features amongst activity classes. SVM and RF are then trained on the PCA feature space to assess classification performance; we undertook an incremental increase in the dataset size.We analyse the classification accuracy, model training and classification time quantitatively at each incremental increase. The experimental results demonstrate that RF outperformed SVM in classification rate for six out of the ten activities. Although SVM has performance advantages in training time, RF would be more suited to real-time activity classification due to its low classification time and high classification accuracy when using eight to ten participants in the training set. © 2013 IEEE

YorkU.pond-impermeable.oct182016.csv

<p><b>Description:</b></p> <p>On Tuesday, October 18th, a study occurred in the pond and impermeable space areas of York University between the Stong Pond and Bergeron Centre on a windy day between 3:00 and 5:00 pm. The sky was cloudy, temperature around 23 Celsius. Four different  surveys were performed using a variety of instruments; a quadrat, 50m of transect tape, sweep nets, and pan traps. Each survey was responsible for recording data for the following: herbaceous plants, woody plants, vertebrates & invertebrates, and invertebrates.</p> <p><b>Methods:</b></p> <p>Every survey performed made use of a transect tape stretched 50 metres in a straight line though the area of study. Each study was also performed twice--once in the grassland, and once in the disturbed area.</p> <p><u>Survey 1 - Herbaceous plants</u></p> <p>Every two metres along the transect, a quadrat was placed on alternating sides of the transect tape. In each quadrat placed, the total number of individuals of native plant species were counted and recorded as well as the total number of exotic species present. In the same quadrat, all observed flower heads were also counted and recorded. This was done along the entire 50 metre length of the transect resulting in 25 repetitions.</p> <p><u>Survey 2 - Woody plants</u></p> <p>Every two metres along the transect, an observer was to look for a tree (any woody plant above 1.5 metres) within 0.5 metres of the transect. At the same points, the observer was to estimate the total overhead canopy coverage, as well as estimating vegetative ground coverage in a square measuring about 0.5x0.5 metres around the transect at that location. The total number of flower heads within 0.5 metres of the point was also recorded. This process was also completed 25 times along the 50 metre length of transect, which also resulted in 25 repetitions</p> <p><u>Survey 3 - Vertebrates & Invertebrates</u></p> <p>At the beginning of the transect, an individual observed a 50 metre radius from their location for 15 minutes, recording any vertebrates they saw and noting the species observed. This was then recorded as total number of individuals seen, as well as total species of vertebrates observed. The same process was repeated for Invertebrates, except the radius was reduced to 5 metres instead. During this survey, any humans observed that were not related to the lab were also recorded. </p> <p> </p> <p> </p> <p><u>Survey 4 - Invertebrates</u></p> <p>Six pan traps of alternating colors (white, yellow and blue) were spaced three metres apart along the length of the transect and filled with a small amount of soapy water to trap invertebrates. Along the length of the transect, a sweep net was performed and at the end of each sweep the amount of invertebrates was recorded. The sweep net was repeated ten times along the transect. At the end of the sweep nets, the pan traps were checked and the abundance of invertebrates caught was recorded.</p> <p> </p> <p><b>Hypothesis - Pond Survey 2:</b></p> <p>The closer to the pond, the greater frequency of observed flowers along the transect.</p> <p><b>Prediction - Pond Survey 2:</b></p> <p>There should be a correlation between the amount of flowers counted and the proximity to the pond because near the pond there is much greater diversity of plants (namely those that flower). Therefore, more flowers would appear close to the pond, and less the farther away.</p> <p> </p> <p><b>Metadata - All Surveys:</b></p> <p>The following metadata is Categorical:</p> <p><u>Campus-</u> The campus the data was obtained from, as there are two universities collecting similar data (UofT and York University).</p> <p><u>Habitat-</u> This is the habitat the data was observed in. The surveys took place around a pond and impermeable habitats.</p> <p><u>Abundance of native plants-</u> The amount of native plants observed in a quadrat during survey 1. A native plant was considered any plant that was not exotic (see exotic plants for detail).</p> <p><u>Abundance of exotic plants</u>- The amount of exotic plants observed in a quadrat during survey 1.  The only exotic plant found in the pond was the wild carrot plant, while in the impermeable habitat, any plant found (except grass) was considered exotic.</p> <p><u>Vertebrate species</u>- The amount of different vertebrate species observed within 50 metres of an observer during survey 3's 15 minute interval. </p> <p> </p> <p>The following metadata is Continuous:</p> <p><u>census</u>- The week of data being sampled. Week 7 is census 2.</p> <p><u>Rep</u>- The amount of repetitions being done during a specific survey.</p> <p><u>Total number of flowers (quadrat)</u>- The total number of flowers of all species observed within the quadrat. A flower was considered any bloom found off of each stem from the main stem of the plant. Dead flower heads were still counted.</p> <p><u>Abundance of woody plants</u>- The amount of trees observed in survey 2 within 0.5  metres of a specific point on the transect. A tree was considered any woody plant measuring 1.5 metres or taller.</p> <p><u>Canopy cover</u>- A estimated percentage of cover provided by nearby tall plants/trees. Recorded by estimating a potion of visual area above observer blocked by foliage.</p> <p><u>Ground cover</u>- The estimated percentage of ground covered by vegetation. Vegetation was considered any plant including grass, but not dead plants.</p> <p><u>Total flower numbers (transect)</u> The total number of flowers of all species observed within 0.5 metres of the location on the transect. A flower was considered any bloom found off of each stem from the main stem of the plant.</p> <p><u>Abundance of vertebrates</u>- The total amount of vertebrates (except humans) observed within 50 metres of an observer during survey 3's 15 minute interval.</p> <p><u>Abundance of humans- </u>The total amount of humans not in the lab observed within 50 metres of an observer during survey 3's 15 minute interval.</p> <p><b> </b></p> <p><u>Abundance of invertebrates in pantraps</u>- The total amount of invertebrates observed within a specific pan trap during survey 4.</p> <p><b> </b></p> <p><u>Abundance of invertebrates in sweeps</u>- The total amount of invertebrates caught during a 50 metre sweep net along the transect.</p> <p><u>Abundance of invertebrates observed-</u> The total amount of invertebrates observed within 5 metres of an observer during survey 3's 15 minute interval.</p> <p><u>Latitude, Longitude and Elevation-</u> Pond: 43.77058, -79.5066 with elevation of 151.3219 meters above sea level. Impermeable: 43.76824, -79.5073 with elevation of 137.7459 meters above sea level.</p> <p><u>Grass- </u>calculated by counting the amount in 1 square cm (5 blades of grass), then multiplied by the size of the quadrat, for a maximum volume of 500 blades of grass. Does not include dead grass. For coverage of grass, the percent coverage was multiplied by 500 to get an approximate number of blades of living grass.</p> <br