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

Semi-automatic Extraction Of Line Features From Aerial Photographs

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2006Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2006Bu çalışmada; dijital hava fotoğrafları üzerinden yollar, binalar, dereler ve göller gibi çizgisel ve alansal detayların sınırlarının ve merkez hatlarının yarı otomatik olarak belirlenmesini sağlayan bir yöntem ve bu yöntemin uygulamaya konmasına yönelik bir yazılım geliştirilmiştir. Otomatik ve yarı otomatik detay belirleme yöntemleri üzerine yapılan çalışmalarda yol izleme ve aktif kontur modeller gibi yöntemler kullanılmakta ve bu yöntemler kullanılan fotoğrafın, düşük veya yüksek çözünürlüğe sahip olmasına bağlı olarak sınıflandırılmaktadır. Geliştirilen yöntem, görüntü bölümleme ve düzey kümesi algoritmalarının birlikte kullanılmasına dayanmaktadır Yöntemin uygulanabilirliğinin araştırılması amacıyla farklı ölçekte ve özelliklerde hava fotoğrafları ve uydu görüntüleri üzerinde yarı otomatik detay çizme işlemleri gerçekleştirilmiştir. Bununla birlikte; İTÜ Ayazağa Kampüsünü içeren ortotofo görüntüler kullanılarak bir doğruluk araştırması yapılıp, yöntemin hata kriterinin ± 0.5m., başka bir deyişle ± 1 piksel olduğu belirlenmiştir. Elde edilen sonuçlar, bu yöntemin harita üretiminde ve coğrafi bilgi sistemleri için veri toplamada kullanılabileceğini göstermiştir.In this study, a semi-automatic extraction method for extracting the center lines and the boundaries of line and area features from digital aerial photographs and a software of this method is developed. Automatic and semi-automatic feature extraction methods are generally using road tracing and snake algorithms and they are classified as the feature extraction methods in low, high and multi resolutions by the resolution of the used digital images. The developed method is based on the image segmentation by the level set algorithms. Some semi-automatic extraction applications are made by the help of the developed software using the different aerial and satellite images for determining the capabilities of this method. Another application for the Ayazağa Campus of İTU, is also carried out. In this application two ortho images, which have 0.5m resolution, are used. An accuracy test is carried out in this application area for determining the accuracy of this method and the square mean root is found for buildings as ± 0.463 m. and for roads as ± 0.663 m. In other words it means ±1 pixel of the image. It can be said as the results of the applications, that this method can be used for producing the maps and for collecting vector data for geographic information systems.DoktoraPh

Primary Pleomorphic Rhabdomyosarcoma of Thyroid Gland in an Adult Patient: A Case Report

Thyroid sarcoma is a very rare entity, accounting for less than 1% of all malignant thyroid tumours. Rhabdomyosarcoma (RMS) is a sarcoma subtype, which is more common in children and adolescents. In this case, a 68-year old man, presented with hoarseness and diagnosed with pleomorphic RMS, was explored. No study of primary thyroid pure RMS has been reported in the literature, with the exception of the case reports of differentiated RMS

Primary Pleomorphic Rhabdomyosarcoma of Thyroid Gland in an Adult Patient: A Case Report

Thyroid sarcoma is a very rare entity, accounting for less than 1% of all malignant thyroid tumours. Rhabdomyosarcoma (RMS) is a sarcoma subtype, which is more common in children and adolescents. In this case, a 68-year old man, presented with hoarseness and diagnosed with pleomorphic RMS, was explored. No study of primary thyroid pure RMS has been reported in the literature, with the exception of the case reports of differentiated RMS

Clinicopathological Characteristics and Prognosis of Patients According to Recurrence Time After Curative Resection for Colorectal Cancer

Purpose: To investigate clinicopathological features in patients with recurrent colorectal cancer within 1 year and more than 1 year after curative resection. Materials and Methods: We retrospectively evaluated 103 patients with disease recurrence before versus after 1 year of resection. Thirty-two patients (31%) were diagnosed with recurrence less than 1 year after curative resection for colorectal cancer (early recurrence) and 71 (69%) after more than 1 year (non-early recurrence). Results: The early recurrence group displayed a significantly lower overall survival rate for both colon cancer (p=0,01) and rectal cancer (p<0.001). Inadequate lymph node dissection was a significant predictor for early relapse. There were no statistically significant differences in clinicopathological variables such as age, sex, primary tumor localization, stage, depth of invasion, lymphovascular invasion and perineural invasion between the early and non-early recurrence groups. However, a K-ras mutation subgroup was significantly associated with early recurrence (p<0.001). Conclusions: Poor survival is associated with early recurrence for patients undergoing resection for non-metastatic colorectal cancer, as well as K-ras mutation