Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or non-vessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous two-dimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces and compare its performance with the linear minimum squared error classifier. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598, being slightly superior than that presented by the method of Staal et al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE Trans Med Imag; added copyright notic

Standard classification of expense

Adopted November 11th, 1920. The basic costs to be ascertained were warehouse, direct shipment and indirect shipment. The total expense of doing business includes all three of these factors. The Committee discussed at length the advisability of following the plan of some cost systems which start with the invoiced unit cost, and then load this with the burden of expense incurred during transit through the various processes involved in filling an order. In following such a procedure, overhead is naturally assessed on a basis of price per pound or some similar unit, but we found the units in a paper warehouse differed so in character that it would be impracticable to follow such a plan. For instance, it developed that some paper warehouses handled not only the various kinds of paper, but ice cream cones, butter dishes, clothes lines, matches, hammocks, automobile tires, etc. For this reason, and also because the majority of houses kept no record of sales and purchases on a tonnage basis, it was found impracticable to operate the system in its initial stages on a price per pound basis. Therefore the only easy, workable method was to operate on a percentage of sales, and the system was devised along this line. Original item in Box no. 040

Simultaneous automatic scoring and co-registration of hormone receptors in tumour areas in whole slide images of breast cancer tissue slides

Aims: Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers in order to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localised scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Methods and Results: Regions of tumour in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883 respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. Conclusions: This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration

Outcome of ATP-based tumor chemosensitivity assay directed chemotherapy in heavily pre-treated recurrent ovarian carcinoma

BACKGROUND: We wished to evaluate the clinical response following ATP-Tumor Chemosensitivity Assay (ATP-TCA) directed salvage chemotherapy in a series of UK patients with advanced ovarian cancer. The results are compared with that of a similar assay used in a different country in terms of evaluability and clinical endpoints. METHODS: From November 1998 to November 2001, 46 patients with pre-treated, advanced ovarian cancer were given a total of 56 courses of chemotherapy based on in-vitro ATP-TCA responses obtained from fresh tumor samples or ascites. Forty-four patients were evaluable for results. Of these, 18 patients had clinically platinum resistant disease (relapse < 6 months after first course of chemotherapy). There was evidence of cisplatin resistance in 31 patients from their first ATP-TCA. Response to treatment was assessed by radiology, clinical assessment and tumor marker level (CA 125). RESULTS: The overall response rate was 59% (33/56) per course of chemotherapy, including 12 complete responses, 21 partial responses, 6 with stable disease, and 15 with progressive disease. Two patients were not evaluable for response having received just one cycle of chemotherapy: if these were excluded the response rate is 61%. Fifteen patients are still alive. Median progression free survival (PFS) was 6.6 months per course of chemotherapy; median overall survival (OAS) for each patient following the start of TCA-directed therapy was 10.4 months (95% confidence interval 7.9-12.8 months). CONCLUSION: The results show similar response rates to previous studies using ATP-TCA directed therapy in recurrent ovarian cancer. The assay shows high evaluability and this study adds weight to the reproducibility of results from different centre

Effect of contextual information on object tracking

Local object information, such as the appearance and motion features of the object, are useful for object tracking in videos provided the object is not occluded by other elements in the scene. During occlusion, however, the local object information in the video frame does not properly represent the true properties of the object, which leads to tracking failure. We propose a framework that combines multiple cues including the local object information, the background characteristics and group motion dynamics to improve object tracking in challenging cluttered environments. The performance of the proposed tracking model is compared with the kernelised correlation filter (KCF) tracker. In the tested video sequences the proposed tracking model correctly tracked objects even when the KCF tracker failed because of occlusion and background noise

Combination of Mean Shift of Colour Signature and Optical Flow for Tracking During Foreground and Background Occlusion

This paper proposes a multiple hypothesis tracking for multiple object tracking with moving camera. The proposed model makes use of the stability of sparse optical flow along with the invariant colour property under size and pose variation, by merging the colour property of objects into optical flow tracking. To evaluate the algorithm five different videos are selected from broadcast horse races where each video represents different challenges that present in object tracking literature. A comparison study of the proposed method, with a colour based mean shift tracking proves the significant improvement in accuracy and stability of object tracking

Improving trial recruitment through improved communication about patient and public involvement : an embedded cluster randomised recruitment trial

Background: Evidence is emerging that patient and public involvement in research (PPIR) may improve recruitment into randomised controlled trials, but the best methods to achieve improvement are unclear. Although many trials use PPIR to improve design and conduct, many do not communicate their use of PPIR clearly to potential participants. Directly communicating PPIR might encourage participation through increased patient confidence and trust in a trial. We aimed to develop and evaluate the impact on recruitment an intervention communicating PPIR in a trial to potential participants. Methods: This study was embedded in EQUIP, a cluster randomised controlled trial which allocated mental health teams in England to either a training intervention group to improve service user and carer involvement in care planning, or to a control group (no training). We conducted a cluster randomised trial of a recruitment intervention communicating PPIR, embedded within the EQUIP trial. The principles underlying the intervention were informed by a systematic review and a workshop that included mental health service users and trialists. Working with EQUIP PPIR partners (service users and carers) we developed the intervention using a leaflet to advertise the nature and function of the PPIR. Professional graphic design optimised readability and impact. Patients identified as potentially eligible for EQUIP were randomised to receive the leaflet or not, alongside the standard trial information. The primary outcome was the proportion of participants enrolled in EQUIP. The secondary outcome was the proportion expressing interest in taking part. Results: 34 clusters (mental health teams) were recruited, and 8182 potential participants were randomised. Preliminary analyses show that for the primary outcome, 4% of patients receiving the PPIR leaflet were enrolled vs. 5.3% in the control group. For the secondary outcome 7.3% of potential participants receiving the PPIR leaflet responded positively to the invitation to participate, vs. 7.9% in the control group. Future analyses will be by intention-to-treat and use logistic regression to estimate between-group odds ratios (ORs) and corresponding 95% confidence intervals. A planned secondary analysis will explore whether the impact of the intervention is moderated by age and gender. Conclusion: In preliminary analysis of this large trial, communicating PPIR demonstrated no benefits for improving the numbers of potential participants expressing interest in the trial, and reduced trial enrolment. Our findings contrast with the literature suggesting PPIR benefits recruitment. We will discuss the potential reasons for this finding, along with implications for future recruitment practice and research

Mobility aids detection using Convolution Neural Network (CNN)

The automated detection of disabled persons in surveillance videos to gain data for lobbying access for disabled persons is a largely unexplored application. We train You Only Look Once (YOLO) CNN on a custom database and achieve an accuracy of 92% for detecting disabled pedestrians in surveillance videos. A person is declared disabled if they are detected in the close proximity of a mobility aid. The detection outcome was further categorised into five classes of mobility aids and precision was calculated

Gait analysis of pedestrians with the aim of detecting disabled people

Gait classification is an effective and non-intrusive method for human identification and it has received significant attention in the recent years due to its applications in visual surveillance and monitoring systems. We analyse gait signatures using spatio-temporal motion characteristics of a person to answer the question ``is there a discriminating feature in the gait signal that can help to categorise a disabled person from healthy?''. The procedure has three steps: detection of a pedestrian using YOLO followed by the silhouette extraction using the Gaussian Mixture Model (GMM). Finally, skeletonization from the silhouette image to estimate head and torso locations and their angles with the vertical axis. Furthermore, velocity and acceleration signals were recorded to look for accelerating behaviour of person walking with a limp. Manual segmentations shows that the gait signal has information about unusual walking patterns but existing pedestrian detectors lack accuracy in extracting an accurate gait signal due to localization errors