A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

MSight: an image analysis software for liquid chromatography-mass spectrometry.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.

Segmentation of brain structures in presence of a space-occupying lesion.

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

L'image de la Suisse à l'étranger

L'image qu'un pays a dans le monde est importante à plusieurs titres. Elle peut soutenir la commercialisation de biens et de services exportés, elle revêt un caractère tout particulier dans le cadre des promotions touristique et économique et elle peut aussi être de nature à contribuer aux relations qu'un pays entretient avec d'autres pays aux niveaux politique, économique ou culturel. L'image de la Suisse a fait l'objet d'études dans de nombreux pays, dont les Etats-Unis, l'Allemagne et la Chine, auprès d'échantillons représentatifs de la population ainsi qu'auprès de groupes de leaders d'opinion et cet ouvrage présente de manière synthétique les principaux résultats de ces études. Après une description de l'image globale de la Suisse auprès des personnes interrogées et une analyse des associations faites à l'évocation de la Suisse, une partie importante est consacrée aux dimensions qui caractérisent l'image du pays en différenciant notamment entre les dimensions liées à la Suisse en tant qu'espace socioculturel et les dimensions liées aux aspects économiques. Pour terminer, un dernier chapitre analyse l'impact de faits ayant marqué l'actualité helvétique, comme le grounding de Swissair, sur l'image de la Suisse dans les pays étudiés.

Underwater Gigamosaicing

El procés de fusió de dues o més imatges de la mateixa escena en una d'única i més gran és conegut com a Image Mosaicing. Un cop finalitzat el procés de construcció d'un mosaic, els límits entre les imatges són habitualment visibles, degut a imprecisions en els registres fotomètric i geomètric. L'Image Blending és l'etapa del procediment de mosaicing a la que aquests artefactes són minimitzats o suprimits. Existeixen diverses metodologies a la literatura que tracten aquests problemes, però la majoria es troben orientades a la creació de panorames terrestres, imatges artístiques d'alta resolució o altres aplicacions a les quals el posicionament de la càmera o l'adquisició de les imatges no són etapes rellevants. El treball amb imatges subaquàtiques presenta desafiaments importants, degut a la presència d'scattering (reflexions de partícules en suspensió) i atenuació de la llum i a condicions físiques extremes a milers de metres de profunditat, amb control limitat dels sistemes d'adquisició i la utilització de tecnologia d'alt cost. Imatges amb il·luminació artificial similar, sense llum global com la oferta pel sol, han de ser unides sense mostrar una unió perceptible. Les imatges adquirides a gran profunditat presenten una qualitat altament depenent de la profunditat, i la seva degradació amb aquest factor és molt rellevant. El principal objectiu del treball és presentar dels principals problemes de la imatge subaquàtica, seleccionar les estratègies més adequades i tractar tota la seqüència adquisició-procesament-visualització del procés. Els resultats obtinguts demostren que la solució desenvolupada, basada en una Estratègia de Selecció de Límit Òptim, Fusió en el Domini del Gradient a les regions comunes i Emfatització Adaptativa d'Imatges amb baix nivell de detall permet obtenir uns resultats amb una alta qualitat. També s'ha proposat una estratègia, amb possibilitat d'implementació paral·lela, que permet processar mosaics de kilòmetres d'extensió amb resolució de centímetres per píxel.

Using a NPWE model observer to assess suitable image quality for a digital mammography quality assurance programme.

A method of objectively determining imaging performance for a mammography quality assurance programme for digital systems was developed. The method is based on the assessment of the visibility of a spherical microcalcification of 0.2 mm using a quasi-ideal observer model. It requires the assessment of the spatial resolution (modulation transfer function) and the noise power spectra of the systems. The contrast is measured using a 0.2-mm thick Al sheet and Polymethylmethacrylate (PMMA) blocks. The minimal image quality was defined as that giving a target contrast-to-noise ratio (CNR) of 5.4. Several evaluations of this objective method for evaluating image quality in mammography quality assurance programmes have been considered on computed radiography (CR) and digital radiography (DR) mammography systems. The measurement gives a threshold CNR necessary to reach the minimum standard image quality required with regards to the visibility of a 0.2-mm microcalcification. This method may replace the CDMAM image evaluation and simplify the threshold contrast visibility test used in mammography quality.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Inexperienced sonographers can successfully visualize and assess a three-dimensional image of the fetal face using a standardized ultrasound protocol

Introduction: A standardized three-dimensional ultrasonographic (3DUS) protocol is described that allows fetal face reconstruction. Ability to identify cleft lip with 3DUS using this protocol was assessed by operators with minimal 3DUS experience. Material and Methods: 260 stored volumes of fetal face were analyzed using a standardized protocol by operators with different levels of competence in 3DUS. The outcomes studied were: (1) the performance of post-processing 3D face volumes for the detection of facial clefts; (2) the ability of a resident with minimal 3DUS experience to reconstruct the acquired facial volumes, and (3) the time needed to reconstruct each plane to allow proper diagnosis of a cleft. Results: The three orthogonal planes of the fetal face (axial, sagittal and coronal) were adequately reconstructed with similar performance when acquired by a maternal-fetal medicine specialist or by residents with minimal experience (72 vs. 76%, p = 0.629). The learning curve for manipulation of 3DUS volumes of the fetal face corresponds to 30 cases and is independent of the operator's level of experience. Discussion: The learning curve for the standardized protocol we describe is short, even for inexperienced sonographers. This technique might decrease the length of anatomy ultrasounds and improve the ability to visualize fetal face anomalies.