Browsing by Subject "Detection methods"

Now showing 1 - 6 of 6

Open Access
Altçizge modellemesi kullanarak kolon bez tespiti
(IEEE, 2011-04) Özgül, Etkin Barış; Sökmensüer, C.; Gündüz-Demir, Çiğdem
Kolon adenokarsinomu, kolon bez yapılarında değişimlere yol açar. Patologlar bezlerdeki bu değişimleri değerlendirerek kolon adenokarsinom tanı ve derecelendirmesi yaparlar. Ancak değişimlerin değerlendirme süreci kaydadeğer öznellik taşıyabilir. Bezlerin matematiksel özniteliklerle karakterize edilmesiyle bu öznelliği azaltabilmek olasıdır. Bunun içinse ilk aşama, bezlerin yerlerinin otomatik olarak tespit edilmesidir. Literatürdeki bez tespit etme yöntemleri çoğunlukla piksel tabanlıdır. Ancak doku görüntüleri, doğaları gereği ve biyopsi hazırlama ve görüntü alma işlemlerindeki değişkenlik nedeni ile piksel bazında değişkenlik gösterebilir. Öte yandan, bu değişkenliğe rağmen, bezleri oluşturan doku bileşenlerinin uzaysal dağılımı benzer özellik gösterir. Bu dağılımı gözönüne alarak tasarlanan yöntemler, bölütleme başarısını artırma potansiyeline sahiptir. Bu çalışmada önerdiğimiz yöntem, ilk olarak, doku bileşenlerinin dağılımını, bu bileşenler üzerinde oluşturduğu bir çizge ile modeller. Daha sonra, oluşturduğu bu çizgeyi altçizgelere ayırır ve bu altçizgelerin öznitelikleri yardımıyla bezleri tespit eder. Kolon doku görüntüleri üzerinde yaptığımız çalışmalar, önerilen bu yöntemin bezlerin yüksek doğrulukta tespit edilmesinde umut verici sonuçlar verdiğini göstermiştir. The colon adenocarcinoma causes changes in glandular structures of colon tissues. Pathologists assess these changes to diagnose and grade the colon adenocarcinoma. However, this assessment may consist of a considerable amount of subjectivity. It is possible to reduce this subjectivity by characterizing the glands with mathematical features. For that, the first step is to detect gland locations. In literature, most of the gland detection methods are pixel-based. However, tissue images may show pixel-level variances due to their nature and differences in biopsy preparation and image acquisition procedures. On the other hand, in spite of these variances, the distribution of tissue components forming glands show similar properties. The methods that consider this distribution has the potential of improving the performance. The method proposed in this study first models the distribution of the components by constructing a graph on them. Then, it breaks the constructed graph down into subgraphs and detects the glands using the features of these subgraphs. The experiments conducted on colon tissue images show that the proposed method leads to promising results for detecting the glands. © 2011 IEEE.
Open Access
Computer vision based forest fire detection
(IEEE, 2008) Töreyin, B. Uğur; Çetin, A. Enis
Lookout posts are commonly installed in the forests all around Turkey and the world. Most of these posts have electricity. Surveillance cameras can be placed on to these surveillance towers to detect possible forest fires. Currently, average fire detection time is 5 minutes in manned lookout towers. The aim ofthe proposed computer vision based method is to reduce the average fire detection rate. The detection method is based on the wavelet based analysis of the background images at various update rates.
Open Access
Detecting user types in object ranking decisions
(ACM, 2009-10) Lu, X.; Schaal, Markus; Adalı, S.; Raju, A. K.
With the emergence of Web 2.0 applications, where information is not only shared across the internet, but also syndicated, evaluated, selected, recombined, edited, etc., quality emergence by collaborative effort from many users becomes crucial. However, users may have low expertise, subjective views, or competitive goals. Therefore, we need to identify cooperative users with strong expertise and high objectivity. As a first step towards this aim, we propose criteria for user type classification based on prior work in psychology and derived from observations in Web 2.0. We devise a statistical model for many different user types, and detection methods for those user types. Finally, we evaluate and demonstrate both model and detection methods by means of an experimental setup. Copyright 2009 ACM.
Open Access
Recent advances in microneedle-based sensors for sampling, diagnosis and monitoring of chronic diseases
(MDPI AG, 2021-08-25) Erdem, Özgecan; İsmail, Eş; Saylan, Y.; Akceoğlu, Garbis Atam; İnci, Fatih
Chronic diseases (CDs) are noncommunicable illnesses with long-term symptoms accounting for ~70% of all deaths worldwide. For the diagnosis and prognosis of CDs, accurate biomarker detection is essential. Currently, the detection of CD-associated biomarkers is employed through complex platforms with certain limitations in their applicability and performance. There is hence unmet need to present innovative strategies that are applicable to the point-of-care (PoC) settings, and also, provide the precise detection of biomarkers. On the other hand, especially at PoC settings, microneedle (MN) technology, which comprises micron-size needles arranged on a miniature patch, has risen as a revolutionary approach in biosensing strategies, opening novel horizons to improve the existing PoC devices. Various MN-based platforms have been manufactured for distinctive purposes employing several techniques and materials. The development of MN-based biosensors for real-time monitoring of CD-associated biomarkers has garnered huge attention in recent years. Herein, we summarize basic concepts of MNs, including microfabrication techniques, design parameters, and their mechanism of action as a biosensing platform for CD diagnosis. Moreover, recent advances in the use of MNs for CD diagnosis are introduced and finally relevant clinical trials carried out using MNs as biosensing devices are highlighted. This review aims to address the potential use of MNs in CD diagnosis.
Open Access
SILVER nano-cylinders designed by EBL used as label free LSPR nano-biosensors
(SPIE, 2011) Cinel, Neval A.; Bütün, Serkan; Özbay, Ekmel
Localized Surface Plasmon Resonance (LSPR) is based on the electromagnetic-field enhancement of metallic nano-particles. It is observed at the metal-dielectric interface and the resonance wavelength can be tuned by the size, shape, and periodicity of the metallic nanoparticles and the surrounding dielectric environment. This makes LSPR a powerful candidate in bio-sensing. In the present work, the size and period dependency of the LSPR wavelength was studied through simulations and fabrications. The surface functionalization, that transforms the surface into a sensing platform was done and verified. Finally, the concentration dependency of the LSPR shifts was observed. All the measurements were done by a transmission set-up. The study is at an early stage, however results are promising. The detection of specific bacteria species can be made possible with such a detection method. © 2011 SPIE.
Open Access
Uydu görüntülerinde düzenli dikim alanlarının belirlenmesi
(IEEE, 2009-04) Yalnız, İsmet Zeki; Aksoy, Selim
Uydu görüntülerindeki dikim alanlarının belirlenmesi, bölütlenmesi, sınıflandırılması ve gözlemlenmesi, bu alanların ekonomik olarak daha iyi kullanım yollarının aranmasına yardımcı olmaktadır. Bir çok insan yapısı gibi, bitkiler de bir düzene göre tarlalarda veya bahçelerde dikilmektedir. Bu bildiride, görüntülerdeki düzen bilgisini kullanarak dikim alanlarını belirleyen bir yöntem önerilmiştir. Bu yöntemde, uydu görüntüsünde nokta filtresinin cevabı üzerinde pencereler gezdirilmekte ve bu pencerelerin izdüşüm vektörleri analiz edilmektedir. Daha sonra bütün pencereler için bir düzenlilik katsayısı belirlenmektedir. Bu düzenlilik katsayıları düzenli alanların daha yüksek değerler aldığı bir düzenlilik haritası çıkartmak için kullanılmaktadır. Bu düzenlilik haritası dikim alanlarının bölütlenmesi ve sınıflandırılması için kullanılabilir. Önerilen yöntem yüksek çözünürlüklü görüntülerde fındık bahçelerinin bulunmasında denenmiş ve sonuçlar tartışılmıştır. Detecting, segmenting and classifying agricultural fields in remote sensing images enable advanced planning of the land use economically. As most human structures, plants are cultivated in some order in orchards or farms. In this paper a regularity detection method is proposed for exploiting this order information. The method slides windows over the spot filter responses of satellite images and analyzes their projection vectors. A regularity coefficient is calculated for each window. These regularity coefficients are further used for creating a regularity map, where regular regions obtain higher scores. These regularity maps can later be employed for the segmentation and classification of cultivation lands. The proposed method is illustrated in the detection of hazelnut orchards in sample high resolution satellite images. ©2009 IEEE.