Diagnosis and salvage surgery of recurrent laryngeal carcinoma after radiotherapy

Bree, R. de [Promotor]Leemans, C.R. [Promotor]Hoekstra, O.S. [Copromotor

Automating the construction of scene classifiers for content-based video retrieval

This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classification (e.g., city, portraits, or countryside). The first stage classifiers can be seen as a set of highly specialized, learned feature detectors, as an alternative to letting an image processing expert determine features a priori. We present results for experiments on a variety of patch and image classes. The scene classifier has been used successfully within television archives and for Internet porn filtering

Multi-Level Visual Alphabets

A central debate in visual perception theory is the argument for indirect versus direct perception; i.e., the use of intermediate, abstract, and hierarchical representations versus direct semantic interpretation of images through interaction with the outside world. We present a content-based representation that combines both approaches. The previously developed Visual Alphabet method is extended with a hierarchy of representations, each level feeding into the next one, but based on features that are not abstract but directly relevant to the task at hand. Explorative benchmark experiments are carried out on face images to investigate and explain the impact of the key parameters such as pattern size, number of prototypes, and distance measures used. Results show that adding an additional middle layer improves results, by encoding the spatial co-occurrence of lower-level pattern prototypes

Real time automatic scene classification

This work has been done as part of the EU VICAR (IST) project and the EU SCOFI project (IAP). The aim of the first project was to develop a real time video indexing classification annotation and retrieval system. For our systems, we have adapted the approach of Picard and Minka [3], who categorized elements of a scene automatically with so-called ’stuff’ categories (e.g., grass, sky, sand, stone). Campbell et al. [1] use similar concepts to describe certain parts of an image, which they named “labeled image regions”. However, they did not use these elements to classify the topic of the scene. Subsequently, we developed a generic approach for the recognition of visual scenes, where an alphabet of basic visual elements (or “typed patches”) is used to classify the topic of a scene. We define a new image element: a patch, which is a group of adjacent pixels within an image, described by a specific local pixel distribution, brightness, and color. In contrast with pixels, a patch as a whole can incorporate semantics. A patch is described by a HSI color histogram with 16 bins and by three texture features (i.e., the variance and two values based on the two eigen values of the covariance matrix of the Intensity values of a mask ran over the image. For more details on the features used we refer to Israel et al. [2]. We aimed at describing each image as a vector with a fixed size and with information about the position of patches that is not strict (strict position would limit generalization). Therefore, a fixed grid is placed over the image and each grid cell is segmented into patches, which are then categorized by a patch classifier. For each grid cell a frequency vector of its classified patches is calculated. These vectors are concate- nated. The resulting vector describes the complete image. Several grids were applied and several patch sizes with the grid cells were tested. Grid size of 3x2 combined with patches of size 16x16 provided the best system performance. For the two classification phases of our system, back-propagation networks were trained: (i) classification of the patches and (ii) classification of the image vector, as a whole. The system was tested on the classification of eight categories of scenes from the Corel database: interiors, city/street, forest, agriculture/countryside, desert, sea, portrait, and crowds. Each of these categories were relevant for the VICAR project. Based upon their relevance for these eight categories of scenes, we choose nine categories for the classification of the patches: building, crowd, grass, road, sand, skin, sky, tree, and water. This approach was found to be successful (for classification of the patches 87.5% correct, and classification of the scenes 73.8% correct). An advantage of our method is its low computational complexity. Moreover, the classified patches themselves are intermediate image representations and can be used for image classification, image segmentation as well as for image matching. A disadvantage is that the patches with which the classifiers were trained had to be manually classified. To solve this drawback, we currently develop algorithms for automatic extraction of relevant patch types. Within the IST project VICAR, a video indexing system was built for the Netherlands Institute for Sound and Vision1, consisting of four independent mod- ules: car recognition, face recognition, movement recognition (of people) and scene recognition. The latter module was based upon the afore mentioned approach. Within the IAP project SCOFI, a real time Internet pornography filter was built, based upon this approach. The system is currently running on several schools in Europe. Within the SCOFI filtering system, our image classification system (with a performance of 92% correct) works together with a text classi- fication system that includes a proxy server (FilterX, developed by Demokritos, Greece) to classify web-pages. Its total performance is 0% overblocking and 1% underblocking

Flap reconstruction of the hypopharynx: a defect orientated approach

The present retrospective analysis evaluated the outcomes of different flap reconstructions for several hypopharyngeal defects in 136 patients who underwent hypopharyngeal reconstruction with a free or pedicled flap after excision of pharyngeal or laryngeal carcinoma.Functional and oncological outcome were the main measures. Nine patients had a type I-a hypopharyngeal defect (partial with larynx preserved), 33 type I-b (partial without larynx preserved), 85 type II (circumferential), 5 type III (extensive superior) and 4 vertical hemipharyngolaryngectomy. The flaps used to reconstruct these defects were pectoralis major (n = 34), free radial forearm (n = 25), jejunum (n = 72), pedicled latissimus dorsi (n = 2), sternocleidomastoid (n = 1), lateral thigh (n = 1) and deltopectoral (n = 1). Twelve defects (9%) needed a secondary flap reconstruction. Surgical and medical complications were seen in 29% and 8% of patients, respectively; 18% of patients developed a fistula. No difference in complication rate or admission days was found for pre-operative versus no previous radiotherapy, type of defect or free versus pedicled flap. After 12 months follow-up, 38% of patients had a tracheo-oesophageal voice prosthesis, in 82% a fully oral diet was obtained and the average body weight gain was 0.9 kg. Five-year overall and disease-specific survival rates were 35% and 49%, respectively, while local and regional control rates were 65% and 91%, respectively. Considering these results, a defect orientated approach may be helpful for deciding which flap should be used for reconstruction of the hypopharynx. An algorithm is proposed with similar functional and oncological outcomes for the different groups. The choice of flap should be based on expected morbidity and functional outcome

Development and sensibility assessment of a health-related quality of life instrument for adults with severe disabilities who are non-ambulatory

Background Insight in health-related quality of life (HRQoL) of adults with severe disabilities who are non-ambulatory is important, but a measure is lacking. The aim was to develop a HRQoL measure for this group. Method The developmental process consisted of the adaptation process of a proxy HRQoL measure for children with severe disabilities who are non-ambulatory and the assessment of the sensibility of the developed instrument. A three-step process was used: focus groups, e-survey and interviews. Results In total, 72% of the items remained unchanged. Three new items and one element to an existing item were added. In ten items, the formulation of the items was adapted to the target group. Concerning the sensibility, respondents suggested minor changes to the instruction and the output scales. Conclusions This study has yielded a proxy HRQoL measure for adults with severe disabilities who are non-ambulatory, the CPADULT, with good sensibility

Belowground Consequences of Intracontinental Range-Expanding Plants and Related Natives in Novel Environments

Introduced exotic plant species that originate from other continents are known to alter soil microbial community composition and nutrient cycling. Plant species that expand range to higher latitudes and altitudes as a consequence of current climate warming might as well affect the composition and functioning of native soil communities in their new range. However, the functional consequences of plant origin have been poorly studied in the case of plant range shifts. Here, we determined rhizosphere bacterial communities of four intracontinental range-expanding plant species in comparison with their four congeneric natives grown in soils collected from underneath those plant species in the field and in soils that are novel to them. We show that, when controlling for both species relatedness and soil characteristics, range-expanding plant species in higher latitude ecosystems will influence soil bacterial community composition and nutrient cycling in a manner similar to congeneric related native species. Our results highlight the importance to include phylogenetically controlled comparisons to disentangle the effect of origin from the effect of contrasting plant traits in the context of exotic plant species

The morality machine: tracking moral values in tweets

This paper introduces The Morality Machine, a system that tracks ethical sentiment in Twitter discussions. Empirical approaches to ethics are rare, and to our knowledge this system is the first to take a machine learning approach. It is based on Moral Foundations Theory, a framework of moral values that are assumed to be universal. Carefully handcrafted keyword dictionaries for Moral Foundations Theory exist, but experiments demonstrate that models that do not leverage these have similar or superior performance, thus proving the value of a more pure machine learning approach.Algorithms and the Foundations of Software technolog