Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis

Abstract. This paper gives an example of evolved features that improve image retrieval performance. A content-based image retrieval system for skin lesion images is presented. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination. Skin lesions of five common classes, including two non-melanoma cancer types, are used. Colour and texture features are extracted from lesions. Evolutionary algorithms are used to create composite features that optimise a similarity matching function. Experiments on our database of 533 images are performed and results are compared to those obtained using simple features. The use of the evolved composite features improves the precision by about 7%.

Lubiprostone Stimulates Duodenal Bicarbonate Secretion in Rats

Lubiprostone, a bicyclic fatty acid, is used for the treatment of chronic constipation. No published study has addressed the effect of lubiprostone on intestinal ion secretion in vivo. The aim of this study was to test the hypothesis that lubiprostone augments duodenal HCO3 − secretion (DBS). Rat proximal duodenal loops were perfused with pH 7.0 Krebs, control vehicle (medium-chain triglycerides), or lubiprostone (0.1–10 μM). We measured DBS with flow-through pH and CO2 electrodes, perfusate [Cl−] with a Cl− electrode, and water flux using a non-absorbable ferrocyanide marker. Some rats were pretreated with a potent, selective CFTR antagonist, CFTRinh-172 (1 mg/kg, ip), 1 h before experiments. Perfusion of lubiprostone concentration dependently increased DBS, whereas net Cl− output and net water output were only increased at 0.1 μM, compared with vehicle. CFTRinh-172 reduced lubiprostone (10 μM)-induced DBS increase, whereas net Cl− output was also unchanged. Nevertheless, CFTRinh-172 reduced basal net water output, which was reversed by lubiprostone. Furthermore, lubiprostone-induced DBS was inhibited by EP4 receptor antagonist, not by an EP1/2 receptor antagonist or by indomethacin pretreatment. In this first study of the effect of lubiprostone on intestinal ion secretion in vivo, lubiprostone stimulated CFTR-dependent DBS without changing net Cl− secretion. This effect supports the hypothesis that Cl− secreted by CFTR is recycled across the apical membrane by anion exchangers. Recovery of water output during CFTR inhibition suggests that lubiprostone may improve the intestinal phenotype in CF patients. Furthermore, increased DBS suggests that lubiprostone may protect the duodenum from acid-induced injury via EP4 receptor activation

Abstract: A database and neural network for highly accurate classification of single bone marrow cells.

Fast and accurate morphological classification of cells in bone marrow samples is a key step in the diagnostic workup of many disorders of the hematopoietic system such as leukemias. In spite of its long-established key position, morphological examination of bone marrow samples has been difficult to automatise, and is still mainly performed manually by trained cytologists on light microscopes. In our contribution [1], we present a neural network for classification of light microscopy images of bone marrow samples

Highly accurate differentiation of bone marrow cell morphologies using deep neuralnetworks on a large image dataset

Biomedical applications of deep learning algorithms rely on large, expert annotated data sets. The classification of bone marrow cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day, due to a lack of datasets and trained models.We apply convolutional neural networks (CNNs) to a large dataset of 171,374 microscopic cytological images taken from bone marrow smears of 945 patients diagnosed with a variety of hematological diseases. The dataset is the largest expert-annotated pool of bone marrow cytology images available in the literature so far. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species at high precision and recall.Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept to the classification problem of single bone marrow cells.This work is a step towards automated evaluation of bone marrow cell morphology using state-of-the-art image classification algorithms. The underlying dataset represents both an educational resource as well as a reference for future AI-based approaches to bone marrow cytomorphology

Vorrichtung und Verfahren zum Adaptieren eines Maskenbildes

(B3) Ein Verfahren zum Adaptieren eines Maskenbildes mit Maskengebiet (110) an ein digitales Bild, umfasst ein Waehlen eines Randpunktes (300), Bestimmen eines unter einer Mehrzahl von vorgegebenen Flaechenelementen S?a? und ein Vergroessern oder ein Verkleinern des Maskengebietes (110), um ein adaptiertes Maskenbild zu erhalten. Das Maskenbild ist durch eine Berandungskurve (130) begrenzt und das digitale Bild weist ein erstes Gebiet (10) mit einem ersten Tonwerttyp und ein zweites Gebiet (20) mit einem zweiten Tonwerttyp auf, und wobei das Maskenbild dem digitalen Bild ueberlagert ist und die Berandungskurve (130) entlang einer Konturkurve (30) verlaufen soll, die das erste Gebiet (10) von dem zweiten Gebiet (20) trennt. Der Randpunkt (300) ist auf der Berandungskurve (130) der Maske, die Flaechenelemente umfassen Flaechenelemente des ersten Tonwerttyps und Flaechenelemente des zweiten Tonwerttyps, so dass das bestimmte Flaechenelement S?k(i)? unter Platzierung desselben an einem Ort des Randpunktes (300) eine Zielfunktion extremal macht, wobei die Zielfunktion von demjenigen Flaechenanteil des jeweiligen Flaechenelementes unter Platzierung desselben an dem Ort des Randpunkts (300) abhaengt, der mit dem ersten Gebiet (10) ueberlappt, falls das jeweilige Flaechenelement vom ersten Tonwerttyp ist und von demjenigen Flaechenanteil des jeweiligen Flaechenelementes unter Platzierung desselben an dem Ort des Randpunkts (300) abhaengt, der mit dem zweiten Gebiet ueberlappt, falls das jeweilige ..

Sensorsystem und Verfahren zur bildgebenden Erfassung eines Objektes

DE 102009007868 B3 UPAB: 20100527 NOVELTY - The system (22) has an imaging sensor (7) i.e. ultrasonic sensor, for receiving of imaging data of an object (13) to be detected. An optical sensor (9) receives image data of the imaging sensor and the object. The imaging sensor is arranged in a receiving region (14) of the optical sensor. An evaluating unit (16) evaluates the imaging data and an image data. The evaluating unit is designed such that relative movement between the imaging sensor and the object is detected from the image data. A three dimensional object image is reconstructed based on the detected relative movement. DETAILED DESCRIPTION - An INDEPENDENT CLAIM is also included for a method for detecting an object. USE - Sensor system for use in an endoscope system (claimed) to detect an object i.e. organ, in medial application. Can also be used for testing a material e.g. textile, foaming and insulation materials, and a component such as air and space technology components and rotor blade of a wind power plant. ADVANTAGE - The design of the system enables simple detection of relative movement between the imaging sensor and the object. The system is provided with high tolerance with respect to input movement of the object