Semi-automatic extraction of line features from aerial photographs

<!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:14.0pt; mso-bidi-font-size:10.0pt; font-family:Arial; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-fareast-language:EN-US;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 70.85pt 70.85pt 70.85pt; mso-header-margin:35.4pt; mso-footer-margin:35.4pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> Bu çalışmada; dijital hava fotoğraflarından çizgisel ve alansal detayların sınırlarının ve merkez hatlarının yarı otomatik olarak belirlenmesini sağlayan bir yöntem ve bu yöntemin uygulamaya konmasına yönelik bir yazılım geliştirilmiştir. Geliştirilen yöntem, görüntü bölümleme ve düzey kümesi algoritmalarının birlikte kullanılmasına dayanmaktadır Yöntemin uygulanabilirliğinin araştırılması amacıyla 1:35000 ölçekli siyah beyaz hava fotoğrafı üzerinde yarı otomatik detay çizme işlemleri gerçekleştirilmiştir. Bununla birlikte; İTÜ Ayazağa Kampüsünü içeren renkli ortofoto görüntüler kullanılarak yöntemin doğruluk araştırması yapılmış ve binalarda ± 0.463m, yollarda ise ± 0.663 karesel ortalama hatalar tespit edilmiştir. Yapılan doğruluk araştırması sonucunda, geliştirilen yöntemin, kullanılan dijital hava fotoğrafının  ±1 pikselinin boyutuna eşit olan bir hata kriterine sahip olduğu sonucuna ulaşılmıştır. Bununla birlikte; bu yöntemin fotogrametrik harita üretiminde ve CBS için fotogrametrik veri sağlanmasında yeni bir yöntem olarak kullanılabileceği değerlendirilmiştir. Özellikle: Göller, sulu dereler ve binalar gibi homojen yapıdaki detayların sınırlarına ait vektör verilerin toplanmasında çok başarılı ve etkili bir şekilde kullanılabileceği görülmüştür. İstenildiği takdirde, tolerans değerinin uygun olarak belirlenmesiyle, söz konusu detaylar üzerinde gözle ayırt edilemeyen sınıflandırmalar ve bölümlemeler gerçekleştirilebileceği tespit edilmiştir. Kaliteli yolların sınırları ve/veya merkez hatları (kullanılan fotoğrafın ölçeğine ve mekânsal ayırma gücüne bağlı olarak) etkili ve hızlı bir şekilde çizilebileceği, ayrıca kırıklık toleransı değerleri değiştirilerek istenilen kırıklıkta vektör veriler elde edilebileceği sonucuna varılmıştır. Raster veriden vektör veriye dönüşümde hem sınırların hem de merkez hatların kullanılabilmesinin etkinliğe çok katkı sağlayacağı düşünülmektedir.   Anahtar Kelimeler: Görüntü bölümleme, düzey kümesi, yarı otomatik, dijital hava fotoğrafı.<!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:14.0pt; mso-bidi-font-size:10.0pt; font-family:Arial; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-fareast-language:EN-US;} p.zetmetni, li.zetmetni, div.zetmetni {mso-style-name:"Özet metni"; margin-top:6.0pt; margin-right:0cm; margin-bottom:0cm; margin-left:0cm; margin-bottom:.0001pt; text-align:justify; mso-pagination:widow-orphan; font-size:11.0pt; mso-bidi-font-size:10.0pt; font-family:Arial; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-fareast-language:EN-US; font-style:italic; mso-bidi-font-style:normal;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 70.85pt 70.85pt 70.85pt; mso-header-margin:35.4pt; mso-footer-margin:35.4pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> Aerial photographs have been evaluated manually by the operators for a long time for the extraction of the vector data. Computer technology and digital image processing technologies have been developed and this development provides to perform these extraction processes automatically or semi-automatically. Automatic feature extraction studies are firstly motivated to carry out the extraction of roads from digital images because roads have characteristic attributes like width, surface type and geometrical shape which can be modelled more easily than the others. The resolution of the images has a very important role in the automatic and semi-automatic extraction of the roads. Most known methods are based on the road tracing and the snakes algorithms. Another method of automatic and semi-automatic feature extraction and classification of images is the image segmentation. In recent years, image segmentation and the front propagation of the segments have been carried out successfully by the Level Set and Fast Marching methods. In this study, a semi-automatic line extraction method, based on the segmentation of the images using color-differences of the pixels and the propagation of fronts by the Level Set algorithms, is developed. An object-oriented application software is also developed to test the capabilities of the developed method. Some semi-automatic feature extraction applications are made by the help of the developed software using a 1:35000 scale black/white aerial photograph for determining the capabilities of this method.  Another application with 1:5000 scaled two ortho images which have 0.5m resolution of Ayazağa Campus of İstanbul Technical University. These ortho images are generated from 1:16000 scaled color aerial photographs. In this test area, an accuracy test is also carried out to find the accuracy of the developed method. In this accuracy test, vector data of roads and buildings are collected semi-automatically with the developed software and also manually with an experienced operator. The data collected by the operator are assumed the correct ones and they are compared with the others collected by the software. The accuracy test is carried out in two groups. In the first group, on 422 road check points, measurements are made and the square mean root found as ±0.663m. In the second group, buildings are used and 281 check points are measured and the square mean root of this group is equal to ±0.463m.As the results of the applications and tests, it can be said that the accuracy of this developed method is ±1 pixel size of the used imagery. It can be used correctly for producing maps and collecting vector data for GIS. Especially for lakes, rivers and buildings can be collected very efficiently. Different classifications and segmentations, which an operator’s can not see, can be made also with the adjusting of the tolerance value. Roads which have good quality can be vectorized from their center lines and/or boundaries according to the scale of the image used. Some weak sides of this developed method and software are also found out. Especially on big scale aerial photographs, the obstacles on the features, as trees, cars and shadows, effects the extraction of the features negatively. Effects of this factor are reduced whether the scale of the image gets smaller. If the tolerance value is not be adjusted to the correct values, wrong features can be extracted. When a big size image is used, the software gives back some errors because the size of the arrays is directly proportional to the number of the pixels. The quality, contrast and noise of the image effect the feature extraction process. The surface attributes of the features also effect the success degree of the feature extraction. If the noise and the contrast of the images are eliminated by the image process algorithms like edge detection algorithms and filters as anisothropic diffusion and the blanks that are generated by the obstacles on the feature can be interpolated by the different kinds of interpolation methods, more good results can be achieved by the developed method and the software. Also, for the image segmentation different types of segmentation like snakes, instead of color difference and for big size images pyramid levels can be used to increase the success degree of this method.   Keywords: Image segmentation, level set, semi-automatic, digital aerial photograph

Clinical and molecular findings in 37 Turkish patients with isolated methylmalonic acidemia

BACKGROUND/AIM: Isolated methylmalonic acidemia is caused by complete or partial deficiency of the enzyme methylmalonyl-CoA mutase (mut0 or mut? enzymatic subtype), a defect of its cofactor adenosyl-cobalamin (cblA, cblB, or cblD-MMA) or deficiency of the enzyme methylmalonyl-CoA epimerase. While onset of the disease ranges from the neonatal period to adulthood, most cases present with lethargy, vomiting and ketoacidosis in the early infancy. Major secondary complications are; growth failure, developmental delay, interstitial nephritis with progressive renal failure, basal ganglia injury and cardiomyopathy. We aimed to demonstrate clinical and molecular findings based on long-term follow up in our patient cohort. MATERIALS AND METHODS: The study includes 37 Turkish patients with isolated MMA who were followed up for long term complications 1 to 14 years. All patients were followed up regularly with clinical, biochemical and dietary monitoring to determine long term complications. Next Generation Sequencing technique was used for mutation screening in five disease-causing genes including; MUT, MMAA, MMAB, MMADHC, MCEE genes. Mutation screening identified 30 different types of mutations. RESULTS: While 28 of these mutations were previously reported, one novel MMAA mutation p.H382Pfs*24 (c.1145delA) and one novel MUT mutation IVS3+1G>T(c.752+1G>T) has been reported. The most common clinical complications were growth retardation, renal involvement, mental motor retardation and developmental delay. Furthermore, one of our patients developed cardiomyopathy, another one died because of hepatic failure and one presented with lactic acidosis after linezolid exposure. CONCLUSIONS: We have detected two novel mutations, including one splice-site mutation in the MUT gene and one frame shift mutation in the MMAA gene in 37 Turkish patients. We confirm the genotype-phenotype correlation in the study population according to the long term complications

Clinical features of 27 Turkish Propionic acidemia patients with 12 novel mutations

Propionic acidemia (PA) is an inherited metabolic disease caused by the deficiency of one of the four biotin-dependent enzymes propionyl-CoA carboxylase (PCC), and is characterized by coma and death in unrecognized patients, additionally late diagnosis leads to severe developmental delay and neurological sequels. Manifestations of PA over time can include growth impairment, intellectual disability, seizures, basal ganglia lesions, pancreatitis, and cardiomyopathy. Other rarely reported complications include optic atrophy, hearing loss, premature ovarian insufficiency, and chronic renal failure. Mutations in PCCA-PCCB genes cause the clinically heterogeneous disease of PA. In this study, we investigate the mutation spectrum of PCCAPCCB genes and phenotypic features of 27 Turkish patients with PA from the South and Southeast parts of Turkey. We report 12 novel PA mutations, five affecting the PCCA gene and 7 affecting the PCCB gene

The outcome of 41 Late-Diagnosed Turkish GA-1 Patients: A Candidate for the Turkish NBS

Background: Glutaric aciduria type 1(GA-1) is an inherited cerebral organic aciduria. Untreated patients with GA-1 have a risk of acute encephalopathic crises during the first 6 years of life. In so far as GA-1 desperately does not exist in Turkish newborn screening (NBS) program, most patients in our study were late-diagnosed. / Method: This study included 41 patients diagnosed with acylcarnitine profile, urinary organic acids, mutation analyses in the symptomatic period. We presented with clinical, neuroradiological, and molecular data of our 41 patients. / Results: The mean age at diagnosis was 14.8 13.9 (15 days to 72 months) and, high blood glutaconic acid, glutarylcarnitine and urinary glutaric acid (GA) levels in 41 patients were revealed. Seventeen different mutations in the glutaryl-CoA dehydrogenase gene were identified, five of which were novel. The patients, most of whom were late-diagnosed, had a poor neurological outcome. Treatment strategies made a little improvement in dystonia and the frequency of encephalopathic attacks. / Conclusion: All GA-1 patients in our study were severely affected since they were latediagnosed, while others show that GA-1 is a treatable metabolic disorder if it is diagnosed with NBS. This study provides an essential perspective of the severe impact on GA-1 patients unless it is diagnosed with NBS. We immediately advocate GA-1 to be included in the Turkish NBS

Mucopolysaccharidosis Type-II with Pathognomonic Skin Appearance: A Case with Pebbling Sign

Mucopolysaccharidosis type-II (MPS-II) is an X-linked lysosomal storage disorder. Here, we report an 8-year-old boy with pebbling sign in the scapular region, coarse facies, claw hand, diastolic murmur, and hepatomegaly. With decreased iduronate-2-sulfatase activity and hemizygous mutation in the IDS gene, the diagnosis was MPS-II. Pebbling sign is a rare but pathognomonic sign of MPS-II

2D recurrent neural networks for robust visual tracking of non-rigid bodies

© Springer International Publishing Switzerland 2016. The efficient tracking of articulated bodies over time is an essential element of pattern recognition and dynamic scenes analysis. This paper proposes a novel method for robust visual tracking, based on the combination of image-based prediction and weighted correlation. Starting from an initial guess, neural computation is applied to predict the position of the target in each video frame. Normalized cross-correlation is then applied to refine the predicted target position. Image-based prediction relies on a novel architecture, derived from the Elman’s Recurrent Neural Networks and adopting nearest neighborhood connections between the input and context layers in order to store the temporal information content of the video. The proposed architecture, named 2D Recurrent Neural Network, ensures both a limited complexity and a very fast learning stage. At the same time, it guarantees fast execution times and excellent accuracy for the considered tracking task. The effectiveness of the proposed approach is demonstrated on a very challenging set of dynamic image sequences, extracted from the final of triple jump at the London 2012 Summer Olympics. The system shows remarkable performance in all considered cases, characterized by changing background and a large variety of articulated motions

Genome Sequencing of SHH Medulloblastoma Predicts Genotype-Related Response to Smoothened Inhibition

SummarySmoothened (SMO) inhibitors recently entered clinical trials for sonic-hedgehog-driven medulloblastoma (SHH-MB). Clinical response is highly variable. To understand the mechanism(s) of primary resistance and identify pathways cooperating with aberrant SHH signaling, we sequenced and profiled a large cohort of SHH-MBs (n = 133). SHH pathway mutations involved PTCH1 (across all age groups), SUFU (infants, including germline), and SMO (adults). Children >3 years old harbored an excess of downstream MYCN and GLI2 amplifications and frequent TP53 mutations, often in the germline, all of which were rare in infants and adults. Functional assays in different SHH-MB xenograft models demonstrated that SHH-MBs harboring a PTCH1 mutation were responsive to SMO inhibition, whereas tumors harboring an SUFU mutation or MYCN amplification were primarily resistant