Adaptive Gray World-Based Color Normalization of Thin Blood Film Images

This paper presents an effective color normalization method for thin blood film images of peripheral blood specimens. Thin blood film images can easily be separated to foreground (cell) and background (plasma) parts. The color of the plasma region is used to estimate and reduce the differences arising from different illumination conditions. A second stage nor- malization based on the database-gray world algorithm trans- forms the color of the foreground objects to match a reference color character. The quantitative experiments demonstrate the effectiveness of the method and its advantages against two other general purpose color correction methods: simple gray world and Retinex

A colour normalization method for giemsa-stained blood cell images

This paper presents a novel method for the colour normalization of Giemsa-stained peripheral blood cell images. The normalization is applied separately to the foreground and background regions. A rough estimation of the foreground-background regions is done by mathematical morphology and followed by a refined segmentation using histograms of these regions. Then an illumination independent response is calculated using the background region. The normalization is completed by transforming the foreground region according to a reference set. The proposed method has been tested on many images and has been found successful

Computer vision for microscopy diagnosis of malaria

This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided

Face Verification Competition on the XM2VTS Database

In the year 2000 a competition was organised to collect face verification results on an identical, publicly available data set using a standard evaluation protocol. The database used was the Xm2vts database along with the Lausanne protocol [14]. Four different institutions submitted results on the database which were subsequently published in [13]. Three years later, a second contest using the same dataset and protocol was organised as part of AVBPA 2003. This time round seven seperate institutions submitted results to the competition. This paper presents the results of the competition and shows that verification results on this protocol have increased in performance by a factor of 3

Parasite detection and identification for automated thin blood film malaria diagnosis

This paper investigates automated detection and identification of malaria parasites in images of Giemsa-stained thin blood film specimens. The Giemsa stain highlights not only the malaria parasites but also the white blood cells, platelets, and artefacts. We propose a complete framework to extract these stained structures, determine whether they are parasites, and identify the infecting species and life-cycle stages. We investigate species and life-cycle-stage identification as multi-class classification problems in which we compare three different classification schemes and empirically show that the detection, species, and life-cycle-stage tasks can be performed in a joint classification as well as an extension to binary detection. The proposed binary parasite detector can operate at 0.1% parasitemia without any false detections and with less than 10 false detections at levels as low as 0.01%

Interactive learning based nodule detection in ct lung volumes [Etkilesimli Ögrenme ile Akciger Tomografi Hacim Taramalarinda Nodül Tespiti]

24th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- -- 122605We present a novel method to automatically detect lung nodules in CT lung scans. Our method is generalized in the sense that it does not assume/depend a particular organ or a particular nodule type. hence it does not require an organ segmentation. We test our method in a challenging set (Anode09) that is comprised of low dose CT scans which include all types of nodules (solid, ground glass opacity, juxta-fissural, juxta-vascular) of less than 10mm in size. Our method produces 8 false positives per scan for true positive rate of 52%, which is comparable to the first 6 results from the contest. © 2016 IEEE

Focusing neuron [Odaklanan Nöron]

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703The traditional neural network topology is not flexible to change during the training process. Every neuron and it's independent weights in the network are part of the solution function. The proposed focusing neuron model utilizes inter-dependent weights produced by a focusing function. This neuron can change it's focus position and aperture. This property allows a flexible-dynamic network topology, which can be trained using conventional back-propagation algorithm. Our experiments show that focusing neuron neural networks achieve higher success than fully connected neural networks. © 2017 IEEE

Ground plane detection using kinect sensor [Microsoft kinect sensörü kullanarak zemin düzlemi algilama]

2013 21st Signal Processing and Communications Applications Conference, SIU 2013 -- 24 April 2013 through 26 April 2013 -- Haspolat -- 98109Ground plane detection is essential for successful navigation of vision based mobile robots. We introduce a novel and robust ground plane detection algorithm using depth information acquired by a Kinect sensor. Unlike similar methods from the literature, we do not assume that the ground plane covers the largest area in the scene. Furthermore our algorithm handles two different conditions: fixed and changing view angle of the sensor. We show that the algorithm is robust if the view angle is fixed whereas an additional procedure handles different view angles satisfactorily. © 2013 IEEE