From Diagnosis to Management; Mucocele of Stump Appendicitis, Extremely Rare Finding in an Uncommon Surgical Disease: Literature Review.

Mucocele of the appendix is the accumulation of mucoid material in the appendiceal lumen. Although the terminology is imprecise, as it does not differentiate between the benign and malignant nature of the condition, preoperative recognition is imperative as spillage of the mucus during surgical handling can result in grave complications like pseudomyxoma peritonei. Mucocele developing in a stump of the appendix, i.e., a remnant of appendiceal tissue after surgical removal of an inflamed organ, is an extremely uncommon phenomenon, as not many cases are reported in the literature. In this review, all cases reported in English literature are discussed.

Peripartum Severe Acute Pancreatitis with Rare Complications: Case Report and Review of Literature.

Peripartum pancreatitis is a rare clinical condition that occurs usually in the third trimester of pregnancy. Pancreatitis is usually secondary to gallstones, and it can lead to life-threatening and rare complications. We report a case of necrotizing postpartum pancreatitis that developed abdominal compartment syndrome (ACS) in early course, posterior reversible encephalopathy syndrome (PRES), and splanchnic and extrasplanchnic thrombosis later on. Case. 31-year-old female, one week after delivery, presented to the emergency department with abdominal pain, nausea and vomiting, tenderness in the epigastrium, and raised pancreatic enzymes. Ultrasound (USG) showed bulky pancreas with gallstones. She was diagnosed as having acute biliary pancreatitis and started to be hydrated and was supplemented with analgesia. Her condition deteriorated on the 2nd day, and she was shifted to the surgical intensive care unit (SICU) where she developed abdominal compartment syndrome (ACS), respiratory distress, and acute kidney injury, requiring endotracheal intubation and ventilation. Computerized tomography (CT) showed pancreatic necrosis with multiple fluid collections and significant left-sided pleural effusion. Percutaneous drainage of pleural effusion was done, and she was stabilized to be weaned off from mechanical ventilation. On day 15, she underwent USG-guided drainage of the pancreatic collection and ERCP (endoscopic retrograde cholangiopancreatography) on day 19. Post-ERCP, she had tonic colonic convulsions which were treated with benzodiazepines and phenytoin. It was diagnosed by imaging studies as posterior reversible encephalopathy syndrome (PRES). Her abdomen was still distended and tender; CT showed a significant pseudocyst with splanchnic and extrasplanchnic thrombosis. She had laparotomy, gastrocystostomy, and cholecystectomy on day 28th. She made uncomplicated recovery and discharged in good health. Conclusion. Peripartum pancreatitis can be complicated by ACS, PRES, and splanchnic and extrasplanchnic thrombosis.

Image segmentation of activated sludge phase contrast images using phase stretch transform.

Activated sludge (AS) is a biological treatment process that is employed in wastewater treatment plants. Filamentous bacteria in AS plays an important role in the settling ability of the sludge. Proper settling of the sludge is essential for normal functionality of the wastewater plants, where filamentous bulking is always a persistent problem preventing sludge from settling. The performance of AS plants is conventionally monitored by physico-chemical procedures. An alternative way of monitoring the AS in wastewater treatment process is to use image processing and analysis. Good performance of the image segmentation algorithms is important to quantify flocs and filaments in AS. In this article, an algorithm is proposed to perform segmentation of filaments in the phase contrast images using phase stretch transform. Different values of strength (S) and warp (W) are tested to obtain optimum segmentation results and decrease the halo and shade-off artefacts encountered in phase contrast microscopy. The performance of the algorithm is assessed using DICE coefficient, accuracy, false positive rate (FPR), false negative rate (FNR) and Rand index (RI). Sixty-one gold approximations of ground truth images were manually prepared to assess the segmentation results. Thirty-two of them were acquired at 10× magnification and 29 of them were acquired at 20× magnification. The proposed algorithm exhibits better segmentation performance with an average DICE coefficient equal to 52.25%, accuracy 99.74%, FNR 41.8% and FPR 0.14% and RI 99.49%, based on 61 images.

Generalized classification modeling of activated sludge process based on microscopic image analysis.

The state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment.

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

Tracking of EEG activity using motion estimation to understand brain wiring.

The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.

Digital image processing and analysis for activated sludge wastewater treatment.

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.