Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge.

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography.

Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.

The role of culture in international students' image formation: the emergence of expected and perceived university image

Tämän tutkimuksen tavoitteena oli selvittää, vaikuttaako kansainvälisen opiskelijan kulttuuritausta opiskelijan odotetun ja koetun yliopistoimagon muodostumiseen. Jotta kulttuurin vaikutuksia yliopistoimagoon voitiin tutkia, tutkimuksessa tunnistettiin yliopistoimagon muodostumiseen oleellisesti vaikuttavat tekijät. Kulttuurin roolia organisaation imagon muodostumisessa ei ole tutkittu aiemmissa tieteellisissä julkaisuissa. Näin ollen tämän tutkimuksen voidaan katsoa edistäneen nykyistä imagotutkimusta. Tutkimuksen kohdeyliopistona oli Lappeenrannan teknillinen yliopisto (LTY). Tutkimuksen empiirinen osa toteutettiin kvantitatiivisena Internet - pohjaisena kyselytutkimuksena tilastollisen analyysin menetelmin. Otos (N=179) koostui kaikista Lappeenrannan teknillisessä yliopistossa lukuvuonna 2005-2006 opiskelleista kansainvälisistä opiskelijoista. Kyselyyn vastasi 68,7 % opiskelijoista. Johtopäätöksenä voidaan todeta, että kulttuurilla ei ole merkittävää vaikutusta yliopistoimagon muodostumiseen. Tutkimuksessa saatiin selville, että yliopiston Internet-sivujen laatu vaikuttaa positiivisesti odotetun yliopistoimagon muodostumiseen, kun taas koettuun yliopistoimagoon vaikuttavat positiivisesti odotettu yliopistoimago, pedagoginen laatu sekä opetusympäristö. Markkinoinnin näkökulmasta tulokset voidaan vetää yhteen toteamalla, että yliopistojen ei tarvitsisi räätälöidä tutkimuksessa tunnistettuja imagoon vaikuttavia tekijöitä eri kulttuureistatulevia opiskelijoita varten.

Multispectral image compression using the wavelet transform

Multispectral images contain information from several spectral wavelengths and currently multispectral images are widely used in remote sensing and they are becoming more common in the field of computer vision and in industrial applications. Typically, one multispectral image in remote sensing may occupy hundreds of megabytes of disk space and several this kind of images may be received from a single measurement. This study considers the compression of multispectral images. The lossy compression is based on the wavelet transform and we compare the suitability of different waveletfilters for the compression. A method for selecting a wavelet filter for the compression and reconstruction of multispectral images is developed. The performance of the multidimensional wavelet transform based compression is compared to other compression methods like PCA, ICA, SPIHT, and DCT/JPEG. The quality of the compression and reconstruction is measured by quantitative measures like signal-to-noise ratio. In addition, we have developed a qualitative measure, which combines the information from the spatial and spectral dimensions of a multispectral image and which also accounts for the visual quality of the bands from the multispectral images.