Segmentation of retinal blood vessels by a novel hybrid technique- Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE)

Diabetic Retinopathy is a persistent disease of eyes that may lead to permanent loss of sight. In this paper, methodology is proposed to segment region of interest (ROI) i.e. new blood vessels in fundus images of retina of Diabetic Retinopathy (DR). The database of 50 fundus retinal images of healthy subjects and DR patients is fetched from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India. The experimental set up consists of three set of experiments for the disease. For DR, in the first stage of automated blood vessel segmentation, gray-scale image is produced from the colored image using Principal Component Analysis (PCA) in the preprocessing step. The contrast enhancement by the Contrast Limited Adaptive Histogram Equalization (CLAHE) highlights the retinal blood vessels in the gray-scale image i.e. it unsheathed newly formed retinal blood vessels whereas PCA preserved their texture and color discrimination in DR images. The expert ophthalmologist(s) scrutiny on both internet repository and real time data acted as the gold standard for further analysis and formation of the proposed method. Further, ophthalmologists ascertained the forming of new blood vessels only on the disc region and divulging them, which were impossible with the naked eye. These operations help in extracting retinal blood vessels present on the disc and non-disc region of the image. The comparison of the results are done with the state of art methods like watershed transform. It is observed from the results that the new blood vessels are better segmented by the proposed methodology and are marked by the experienced ophthalmologist for validation. Further, for quantitative analysis, the features are extracted from new blood vessels as they are crucial for scientific interpretation. The results of the features lie in permissible limits such as no. of segments vary from 2 to 5 and length of segments varies from 49 to 164 pixels. Similarly, other features such as gray level of new blood vessels lie in 0.296-0.935 normalized range, coefficient with variations in gray level in the range of 0.658-10.10 and distance from vessel origin lie in the range of 56-82 pixels respectively. Both quantitative and qualitative results show that the methodologies proposed boosted the ophthalmic and clinical diagnosis. The developed method further handled the false detection of vessels near the optic disk boundary, under-segmentation of thin vessels, detection of pathological anomalies such as exudates, micro-aneurysms and cotton wool spots. From the numerical analysis, ophthalmologist extracted the information of number of vessels formed, length of the new vessels, observation that the new vessels appearing are less homogenous than the normal vessels. Also about the new vessels, whether they lie on the centre of disc region or towards its edges. These parameters lie as per the findings of the ophthalmologists on retinal images and automated detection helped in monitoring and comprehensive patient assessment. The experimental results show case that the proposed method has higher sensitivity, specificity and accuracy as compared to state of art methods i.e. 0.9023, 0.9610 and 0.9921, respectively. Similar results are obtained on retinal fundus images of PGIMER Chandigarh with sensitivity-0.9234, specificity-0.9955 and accuracy-0.9682.

Multimodal Medical Image Fusion Using Nonsubsampled Shearlet Transform and Smallest Uni-Value Segment Assimilating Nucleus

This paper presents a new fusion scheme for medical (CT-MRI) images which is based on the nonsubsampled shearlet transform (NSST). The various image pairs to be fused are obtained from primary and internet sources. Initially, the images are decomposed through NSST into general and detailed features. The smallest uni-value segment assimilating nucleus (SUSAN) and local sum of Gaussian weighted pixel intensities-based activity measures are proposed to fuse the detailed sub-bands and low-frequency sub-band of NSST, respectively, for faster execution of the algorithm. Visual and parametric comparison of the proposed scheme is done through five traditional fusion algorithms using nine fusion performance parameters. In addition, Wilcoxon signed ranks test is also applied to compare different methods scientifically with the proposed fusion scheme. It is observed that the presented method is better in retaining bone, calcification, cerebrospinal fluid (CSF), edema and tumor details of the source images and is faster than other classical fusion schemes. The fused images of the proposed method are suitable for locating the site of biopsy externally or incision location in the bone of the brain skull with minimum diagnostic time.

Access to care and frequency of detransition among a cohort discharged by a UK national adult gender identity clinic: retrospective case-note review

BACKGROUND

UK adult gender identity clinics (GICs) are implementing a new streamlined service model. However, there is minimal evidence from these services underpinning this. It is also unknown how many service users subsequently 'detransition'.

AIMS

To describe service users' access to care and patterns of service use, specifically, interventions accessed, reasons for discharge and re-referrals; to identify factors associated with access; and to quantify 'detransitioning'.

METHOD

A retrospective case-note review was performed as a service evaluation for 175 service users consecutively discharged by a tertiary National Health Service adult GIC between 1 September 2017 and 31 August 2018. Descriptive statistics were used for rates of accessing interventions sought, reasons for discharge, re-referral and frequency of detransitioning. Using multivariate analysis, we sought associations between several variables and 'accessing care' or 'other outcome'.

RESULTS

The treatment pathway was completed by 56.1%. All interventions initially sought were accessed by 58%; 94% accessed hormones but only 47.7% accessed gender reassignment surgery; 21.7% disengaged; and 19.4% were re-referred. Multivariate analysis identified coexisting neurodevelopmental disorders (odds ratio [OR] = 5.7, 95% CI = 1.7-19), previous adverse childhood experiences (ACEs) per reported ACE (OR = 1.5, 95% CI = 1.1-1.9), substance misuse during treatment (OR = 4.3, 95% CI = 1.1-17.6) and mental health concerns during treatment (OR = 2.2, 95% CI 1.1-4.4) as independently associated with accessing care. Twelve people (6.9%) met our case definition of detransitioning.

CONCLUSIONS

Service users may have unmet needs. Neurodevelopmental disorders or ACEs suggest complexity requiring consideration during the assessment process. Managing mental ill health and substance misuse during treatment needs optimising. Detransitioning might be more frequent than previously reported.

Clinical and radiological predictors of outcome in tubercular meningitis: A prospective study of 209 patients

OBJECTIVES

The predictors of poor outcome in tuberculous meningitis (TBM) remain to be delineated. We determined role of various clinical, radiological and cerebrospinal fluid (CSF) parameters in prediction of outcome in TBM.

PATIENTS AND METHODS

Current study was a prospective observational study including 209 patients of TBM. All patients underwent detailed evaluation including Gadolinium enhanced Magnetic resonance imaging (GdMRI) of brain as well as tests to detect evidence of tuberculosis elsewhere in body. They also underwent GdMRI at three and nine month follow up. All patients received treatment as per standard guidelines.

RESULTS

Mean age was 30.4±13.8years. 139 (66.5%) patients had definite TBM while 70 (34.5%) had highly probable TBM. 53 (25.4%) patients died. On univariate analysis, longer duration of illness, altered sensorium, stage III TBM, hydrocephalus and exudates correlated with poor outcome. On multivariate analysis presence of hydrocephalus (p=0.003; OR=3.2; 95% CI=1.5-6.7) and stage III TBM (p<0.0001; OR=8.7; 95% CI=3.7-20.2) correlated with higher risk of mortality. In addition, there was significant positive association between presence of hydrocephalus (p=0.05; OR=2.2; 95% CI=0.97-5.1), stage III TBM (p<0.0001; OR=28; 95% CI=4.9-158) and presence of altered sensorium (p=0.05; OR=22; 95% CI=0.99-4.8) with either death or survival with severe disability.

CONCLUSIONS

It is possible to prognosticate TBM using a combination of clinical and radiological. The duration of illness (65.9±92days) before diagnosis of TBM continues to be unacceptably long and this stresses on need to educate primary care physicians about TBM. Future studies where intensity and duration of treatment is guided by these cues may help in sorting out some of the most difficult questions in TBM, namely duration of antitubercular therapy as well as dose and duration of steroid therapy etc.

Early hematological effects of chemo-radiation therapy in cancer patients and their pattern of recovery - A prospective single institution study

The purpose of this prospective study is to understand the early hematological effects of chemo-radiation therapy in cancer patients, their pattern of recovery and to ascertain their prognostic value.

METHODS

255 diagnosed cancer patients planned for definitive treatment with radiation therapy alone or with chemotherapy were included in this two year prospective study. A complete blood count was done at baseline, weekly during the course of therapy and thereafter, monthly for a period of 6 months. For the purpose of grading clinical toxicity, the Common Toxicity Criteria, CTCAE v2.0 was used while RECIST criteria was used to define the tumor response rates. This study was statistically analyzed using SPSS software.

RESULTS

255 patients were included in the study wherein head and neck cancers comprised the major patient population (28.6%) followed by cervix (18.8%) and breast (15.7%). Out of these, 37% in head-and-neck cancer subgroup, and 58.3% in cervix had anemia at start of treatment. 92.2% cases with chemoradiation developed anemia during treatment, while with radiation alone it was 95.5%. This was statistically significant in patients with cancer uterine cervix (p 〈 0.01). At the end of treatment 65% patients with normal hemoglobin had complete responses (CR), while 58.3% with mild anemia and 33.3% with moderate anemia had CR (p=0.1).

CONCLUSIONS

Severe anemia during treatment is a poor prognostic indicator and is usually a sign of advanced disease. Leucopenia and thrombocytopenia occur more commonly during chemoradiotherapy as against radiotherapy alone, but improves with supportive management.

Segmentation, feature extraction, and multiclass brain tumor classification

Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %. PCA-ANN has delivered high accuracy for each class: AS-90.74 %, GBM-88.46 %, MED-85 %, MEN-90.70 %, MET-96.67 %, and NR-93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS-86.15 %, GBM-65.1 %, MED-63.36 %, MEN-91.5 %, MET-65.21 %, and NR-93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.

A dual neural network ensemble approach for multiclass brain tumor classification

The present study is conducted to develop an interactive computer aided diagnosis (CAD) system for assisting radiologists in multiclass classification of brain tumors. In this paper, primary brain tumors such as astrocytoma, glioblastoma multiforme, childhood tumor-medulloblastoma, meningioma and secondary tumor-metastases along with normal regions are classified by a dual level neural network ensemble. Two hundred eighteen texture and intensity features are extracted from 856 segmented regions of interest (SROIs) and are taken as input. PCA is used for reduction of dimensionality of the feature space. The study is performed on a diversified dataset of 428 post contrast T1-weighted magnetic resonance images of 55 patients. Two sets of experiments are performed. In the first experiment, random selection is used which may allow SROIs from the same patient having similar characteristics to appear in both training and testing simultaneously. In the second experiment, not even a single SROI from the same patient is common during training and testing. In the first experiment, it is observed that the dual level neural network ensemble has enhanced the overall accuracy to 95.85% compared with 91.97% of single level artificial neural network. The proposed method delivers high accuracy for each class. The accuracy obtained for each class is: astrocytoma 96.29%, glioblastoma multiforme 96.15%, childhood tumor-medulloblastoma 90%, meningioma 93.00%, secondary tumor-metastases 96.67% and normal regions 97.41%. This study reveals that dual level neural network ensemble provides better results than the single level artificial neural network. In the second experiment, overall classification accuracy of 90.4% was achieved. The generalization ability of this approach can be tested by analyzing larger datasets. The extensive training will also further improve the performance of the proposed dual network ensemble. Quantitative results obtained from the proposed method will assist the radiologist in forming a better decision for classifying brain tumors.

Factors influencing posttransplantation employment: does depression have an impact?

BACKGROUND

Depressive disorders are the leading cause of disability in the United States. Liver transplant recipients often have significant psychiatric morbidity, including depression. One of the potential consequences of depression is the inability to work.

OBJECTIVE

The objective of this study was to determine if there is any relationship between depression and posttransplantation employment status in liver transplant recipients.

METHODS

Patients, 18 years of age or older, who had received liver transplants from January 2007 to July 2009 were identified for the retrospective analysis. Individual posttransplantation patient charts were reviewed for patient demographics, transplantation indication, employment history, depression diagnosis, and medications. The pretransplantation charts were used to obtain family psychiatric history, patient psychiatric history, past drug, alcohol, and tobacco use, and pretransplantation employment status.

RESULTS

A total of 91 patients were evaluated, of which 59.3% were males and 40.7% were females, with a mean age of 56 years. In our sample, 23% and 29% of patients were depressed pretransplantation and posttransplantation, respectively. The number of unemployed patients also increased from 10.9%-23.1%. A logistic regression was performed to identify the factors influencing employment posttransplantation, which indicated pretransplantation employment, gender (males more likely to return to work), and depression post transplantation as significant factors with odds rations of 128, 4.1, and 11.5 and corresponding P values of <.0001, .04 and .008, respectively.

CONCLUSION

Posttransplantation depression is significantly associated with post-liver transplantation unemployment. Improved management of depression may facilitate a patient's return to work after transplantation.