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Abstrak :
[ABSTRAK
Kanker payudara adalah tumor ganas yang tumbuh akibat pertumbuhan sel-sel jaringan yang tidak normal pada jaringan payudara. Kanker payudara pada wanita merupakan penyakit yang kini paling banyak diderita dibandingkan jenis kanker lainnya. Cara yang dilakukan agar penyakit ini tidak memiliki kesempatan untuk menyebar adalah dengan mendeteksinya sedini mungkin dengan menggunakan mammografi.

Pada penelitian ini penulis telah merancang suatu sistem yang menggunakan komputer untuk mendeteksi dan mengklasifikasi kanker payudara pada citra mammogram. Citra mammogram yang digunakan adalah citra mammogram dari Mommographic Image Analysis Society (MIAS) yang terdiri dari 322 citra. Pengolahan awal citra pada sistem ini menggunakan metode Otsu Thresholding, pendeteksian tepi dengan menggunakan metode Canny, dan metode dilasi. Ciri yang digunakan pada sistem ini adalah Gray Level Co-occurrence Matrix (GLCM) dan Discrete Wavelet Transform (DWT). Metode pengklasifikasian yang digunakan pada penelitian ini adalah Support Vector Machine (SVM). Sistem memiliki ketahanan yang baik terhadap noise salt and pepper pada nilai noise tertentu pada tiap jenis citra mammogram yang digunakan. Tingkat keakuratan berkisar 80% pada saat diberi noise sebesar -16dB pada citra mammogram jinak dan ganas. Keakuratan sistem juga teruji cukup baik untuk jumlah data latih yang hanya sebesar 70% dimana tingkat keakuratan pendeteksian dan pengklasifikasian adalah sebesar 80,6%.

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ABSTRACT
Breast cancer is a malignant tumor that grows as a result of the growth of tissue cells that are not normal in the breast tissue. Breast cancer in women is a disease that is now the most common cancer than other types. How that is done so that the disease does not have a chance to spread is to detect it as early as possible by using mammography.

In this study, the authors have designed a system that uses a computer to detect and classify breast cancer on a mammogram image. Mammogram image has been taken from Mommographic Image Analysis Society (MIAS) which consists of 322 images. Initial processing images on this system using Otsu Thresholding, edge detection using Canny method, and the method of dilation. Features used in this system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). Claassification method was used in this study is Support Vector Machine (SVM).

The system has good resistance to salt and pepper noise on certain noise value for each type of mammogram image are used. The accuracy range was 80% when given the noise of -16dB on mammogram images of benign and malignant. The accuracy of the system was also tested well enough for the amount of training data that only 70% where the level of detection and classification accuracy is 80,6 %.;Breast cancer is a malignant tumor that grows as a result of the growth of tissue cells that are not normal in the breast tissue. Breast cancer in women is a disease that is now the most common cancer than other types. How that is done so that the disease does not have a chance to spread is to detect it as early as possible by using mammography. In this study, the authors have designed a system that uses a computer to detect and classify breast cancer on a mammogram image. Mammogram image has been taken from Mommographic Image Analysis Society (MIAS) which consists of 322 images. Initial processing images on this system using Otsu Thresholding, edge detection using Canny method, and the method of dilation. Features used in this system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). Claassification method was used in this study is Support Vector Machine (SVM). The system has good resistance to salt and pepper noise on certain noise value for each type of mammogram image are used. The accuracy range was 80% when given the noise of -16dB on mammogram images of benign and malignant. The accuracy of the system was also tested well enough for the amount of training data that only 70% where the level of detection and classification accuracy is 80,6 %., Breast cancer is a malignant tumor that grows as a result of the growth of tissue cells that are not normal in the breast tissue. Breast cancer in women is a disease that is now the most common cancer than other types. How that is done so that the disease does not have a chance to spread is to detect it as early as possible by using mammography. In this study, the authors have designed a system that uses a computer to detect and classify breast cancer on a mammogram image. Mammogram image has been taken from Mommographic Image Analysis Society (MIAS) which consists of 322 images. Initial processing images on this system using Otsu Thresholding, edge detection using Canny method, and the method of dilation. Features used in this system is the Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT). Claassification method was used in this study is Support Vector Machine (SVM). The system has good resistance to salt and pepper noise on certain noise value for each type of mammogram image are used. The accuracy range was 80% when given the noise of -16dB on mammogram images of benign and malignant. The accuracy of the system was also tested well enough for the amount of training data that only 70% where the level of detection and classification accuracy is 80,6 %.]

Abstrak :
ABSTRAK
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Diabetes merupakan penyakit kronis yang terjadi ketika terdapat peningkatan kadar glukosa dalam darah karena tubuh tidak dapat atau tidak cukup menghasilkan hormon insulin atau tidak dapat menggunakan insulin secara efektif. Umumnya untuk mendeteksi penyakit diabetes adalah dengan tes kadar gula darah atau hemoglobin HbA1c yang dilakukan oleh praktisi medis. Pada penelitian ini, dibangun sistem prediksi penyakit diabetes berbasis iridologi atau melalui citra mata, menggunakan machine learning. Sistem yang dikembangkan terdiri dari instrumen akuisisi citra mata dan algoritma pengolahan citra. Metode GLCM (Gray Level Co-Occurence Matrix) digunakan untuk proses ekstraksi ciri, dengan tujuan untuk mendapatkan ciri tekstur pada citra. Metode SVM (Support Vector Machine) dan kNN (k Nearest Neighbor) digunakan untuk proses klasifikasi kelas diabetes dan non-diabetes. Hasil klasifikasi kemudian dilakukan proses validasi dengan menggunakan metode k-fold cross validation. Hasil yang diperoleh menunjukkan bahwa metode kNN memiliki performa yang lebih baik dibandingkan dengan metode SVM. Performa terbaik didapatkan saat variasi kombinasi ukuran area segmentasi 30×360 dengan jarak antar tetangga 30 pixel. Tingkat akurasi yang diapatkan dari pengujian sebesar 79,6%, dengan nilai misclassification rate (MR) 20,4%, false positive rate (FPR) 20,6%, false negative rate (FNR) 20%, sensitivity 87,1%, dan specificity 70,0%.

 

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ABSTRACT

Diabetes is a chronic disease that occurs when there is an increase in glucose levels in the blood because the body cannot produce enough of the hormone insulin or cannot use insulin effectively. Generally, to detect diabetes is by pengujian blood sugar levels or hemoglobin HbA1c carried out by medical practitioners. In this study, a diabetes prediction system based on iridology or through eye images was constructed using machine learning. The developed system consists of eye image acquisition instruments and image processing algorithms. The GLCM (Gray Level Co-Occurence Matrix) method is used for feature extraction processes, with the aim of obtaining texture characteristics in the image. The SVM (Support Vector Machine) and kNN (k Nearest Neighbor) methods are used to classify diabetic and non-diabetic classes. The classification results are then validated by using the k-fold cross validation method. The results show that kNN method has better performance compared to the SVM method. The best performance is when size of the segmentation area 30×360 pixel with the distance between neighbors 20 pixel. The results show that the accuracy from pengujian is 79.6%, misclassification rate (MR) 20.4%, false positive rate (FPR) 20.6%, false negative rate (FNR) 20.0%, sensitivity 87.1%, and specificity 70.0%.

 

Abstrak :
Kanker liver pada citra hasil CT-Scan memiliki bentuk, lokasi serta tekstur yang berbeda – beda disetiap citra. Perbedaan contrast antara abnormalitas dan liver sehat sering kali tidak dapat terlihat jelas, sehingga menyulitkan dalam evaluasi. Abnormalitas liver antara lain pembengkakan, fibrosis, kehadiran tumor jinak atau tumor ganas. Perbedaan contrast rendah dengan ukuran lebar dalam citra mudah dikenali sebagai abnormalitas, namun untuk massa kecil dan contrast rendah sulit dievaluasi. Dalam penelitian ini telah dilakukan CAD dengan tujuan untuk membantu evaluasi abnormalitas liver utamanya abnormalitas dengan ukuran kecil. Metode penelitian yang digunakan dalam penelitian ini adalah metode segmentasi berdasarkan active contour. Data yang digunakan merupakan data sekunder citra abdomen yang dihasilkan dari modalitas Computed Tomography Scanner (CT-Scan) RSUD Cibinong Bogor. Teknik pengumpulan data yang digunakan dengan melakukan observasi pada data pasien citra liver abnormal dari pasien-pasien kanker liver dan citra liver normal dari pasien-pasien penyakit lainnya sesuai dengan diagnosis dokter. Sedangkan untuk olah data digunakan proses ekstraksi fitur menggunakan analisis tekstur Gray-Level Co-occurrence Matrix (GLCM) dengan machine learning berupa Artificial Neural Network (ANN) untuk deteksi abnormalitas citra. Hasil penelitian menyatakan bahwa ANN dapat digunakan untuk mengelompokkan citra kedalam grup normal dan abnormal dengan akurasi sebesar 89% sensitivitas 86%, spesifisitas 92%, presisi 91%, error keseluruhan 10%. ......Liver abnormalities in CT image commonly have different shape, location and texture. The contrast between abnormalities and healthy liver often cannot be clearly seen, making it difficult to evaluate. Liver abnormalities include swelling, fibrosis, the presence of benign tumors or malignant tumors. Low contrast differences with width measurements in images are easily recognized as abnormalities, but for small masses and low contrast it is difficult to evaluate. In this study CAD was carried out with the aim to help evaluate liver abnormalities, especially small size abnormalities. The segmentation method based on active contour is the method was employed in this research. The data which used was secondary data resulting abdomen image  from modalities of Computed Tomography Scanner (CT-Scan) of Cibinong Hospital, Bogor. The data collection techniques was used in this research were data abnormal liver image from patients liver cancer and normal liver image from patients other diseases according to the doctor's diagnosis. Meanwhile, the technique used to processing data was extraction feature process with analysis Gray-Level Co-occurrence Matrix (GLCM) texture and machine learning of Artificial Neural Network (ANN) for detection abnormality image. Results of this research stated that ANN can used for classify image to normal and abnormal group with accuracy of 89%, sensitivity of 86%, specificity of 92%, precision of 91%, and error of 10%.

Abstrak :
ABSTRAK
Kadar kolesterol yang tinggi dalam darah dapat memicu timbulnya penyakit jantung koroner. Berdasarkan data yang diperoleh dari Kementerian Kesehatan Republik Indonesia, penyakit jantung koroner merupakan penyebab kematian tertinggi kedua setelah stroke dengan persentase 12.9% pada tahun 2014. Selain kolesterol tinggi, kondisi stres yang tinggi juga dapat memicu berbagai penyakit seperti gangguan pencernaan, kecemasan, dan gangguan jantung. Sehingga pemeriksaan kesehatan sedini mungkin baik dengan metode alternatif maupun pemeriksaan secara medis perlu dilakukan.

Penelitian ini membahas mengenai deteksi kolesterol dan stres melalui pengamatan citra iris. Endapan lemak yang telah terbentuk di jaringan kornea menghasilkan keburaman di area terluar iris. Tanda ini merupakan indikasi dari ketidakseimbangan tubuh sebagai tanda kolesterol berlebih. Sedangkan tidak terbentuknya endapan lemak mengindikasikan kondisi kolesterol tidak tinggi. Sehingga dari pengamatan karakteristik iris ini, dapat dideteksi kondisi kolesterol tinggi dan kolesterol tidak tinggi. Lingkaran-lingkaran yang terbentuk pada iris atau yang disebut dengan cincin saraf mengindikasikan adanya ketegangan saraf berlebih. Cincin saraf terbentuk karena adanya iritabilitas, insomnia, ketidakseimbangan mental dan emosi seseorang. Sehingga tanda ini dapat mengindikasikan kondisi stres seseorang berupa bergejala stres atau tidak bergejala.

Deteksi kolesterol dan stres ini dibuat menggunakan metode Morphology Reconstruction untuk mengubah karakteristik penyakit lain pada ROI yang sama, Gray Level Co-occurence Matrix (GLCM) sebagai metode ekstraksi ciri, dan Backpropagation Neural Network (BNN) sebagai metode klasifikasi. Ciri yang digunakan dalam penelitian ini adalah entropy, contrast, correlation, energy, homogeneity, variance, dan difference variance. Dari hasil perancangan dengan jumlah citra pelatihan masing-masing sebesar 59 untuk deteksi kolesterol dan 53 untuk deteksi stres, diperoleh tingkat akurasi pengujian mencapai 96.49% untuk deteksi kolesterol dan 85.96% untuk deteksi stres dengan jumlah citra uji sebesar 57 citra.

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ABSTRACT
High cholesterol levels in the blood can trigger coronary heart disease. Based on data obtained from the Ministry of Health of the Republic of Indonesia, coronary heart disease is the second highest cause of death with a percentage of 12.9% in 2014. Besides high cholesterol, high stress conditions can also trigger various diseases such as digestive disorders, anxiety, and heart problems. So people need to do health examinations as early as possible. This study discusses the detection of cholesterol and stress through observation of iris images. Fat deposits that have formed in the corneal tissue produce blur in the outer area of the iris. This sign is an indication of body imbalance as a sign of excess cholesterol. While the formation of fat deposits does not indicate the condition of cholesterol, it is identified as not high cholesterol. So from observing the characteristics of this iris, high cholesterol and not high cholesterol conditions can be detected. The circles that form on the iris or called as nerve ring indicate excessive nervous tension. The nerve ring is formed due to irritability, insomnia, mental and emotional imbalance in a person. So this sign can indicate a person's stress condition in the form of symptomatic stress or asymptomatic.

This cholesterol and stress detection is made using the Morphology Reconstruction method to change the characteristics of other diseases on the same Region of Interest, Gray Level Co-occurrence Matrix (GLCM) as a feature extraction method, and Backpropagation Neural Network (BNN) as a classification method. The characteristics used in this study are entropy, contrast, correlation, energy, homogeneity, variance, and difference variance. From the results of the design with the number of training images respectively 59 images for cholesterol detection and 53 images for stress detection, the accuracy of the test is 96.49% for cholesterol detection and 85.96% for stress detection with the number of testing images is 57 images.

Abstrak :
World Health Organisation (WHO) has predicted 300 million peoples will suffer of diabetic in 2025. Long-term diabetics can lead to diabetic retinopathy that can cause blindness in developing countries. One of the abnormalities of diabetic retinopathy is exudate. Exudates are classified into two categories, i.e. hard and soft exudates. This paper proposes feature extraction based on texture for distinguishing hard, soft and non-exudates. The green channel of the original images is enhanced by CLAHE and followed by median filtering and thresholding in red channel to detect and remove the optic disc. The enhanced image is segmented based on clustering to obtain the region of interest of exudates. Feature extraction based on texture is conducted by using GLCM and lacunarity. Results show that classification based on NaïveBayes algorithm achieves accuracy, specificity and sensitivity of 92.13%, 96% and 87.18%, respectively.

Abstrak :
ABSTRAK
Tesis ini membahas kemampuan program segmentasi dan klasifikasi untuk melokalisasi daerah terduga kanker serviks dan mengelompokkan antara citra normal dan abnormal berdasarkan fitur-fitur yang terdapat dalam suatu citra. Citra yang digunakan terdiri dari dua macam, yaitu citra serviks abnormal dari pasien-pasien kanker serviks dan citra serviks normal dari pasien-pasien penyakit lainnya. Beberapa parameter dasar digunakan untuk mengklasifikasikan data citra ke dalam kelompok Abnormal dan Normal, yaitu panjang serviks, distribusi nilai piksel, jumlah piksel dan volume serviks pada citra CT-Scan. Namun, parameter-parameter tersebut memberikan hasil klasifikasi yang kurang akurat. Solusi yang ditawarkan adalah mensegmentasi daerah serviks dan mendapatkan fitur-fitur tekstur daerah tersebut pada data citra CT-Scan. Algoritma segmentasi yang digunakan adalah edge detection dan region-based snake model. Proses ekstraksi fitur menggunakan analisis tekstur Gray-Level Co-occurrence Matrix GLCM dengan machine learning berupa Support Vector Machine SVM . Hasil penelitian menyatakan bahwa SVM dapat digunakan untuk mengelompokkan citra ke kelompok normal dan abnormal dengan sensitivitas 95,2 , spesifisitas 90,5 , akurasi 92,9 , presisi 90,9 dan error keseluruhan 7,1.

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ABSTRACT
The focus of this study is discussing the ability of segmentation and classification programs to localize areas of cervical cancer and to classify image data to normal and abnormal group based on features contained in images. Image data consists of two kinds, abnormal cervical images of cervical cancer patients and normal cervical images from patients of other diseases. Some basic parameters are used to classify image data into Abnormal and Normal groups, ie. cervical length, pixel value distribution, number of pixels and cervical volume on CT Scan images. However, these parameters give inaccurate classification results. The offered solution is to segment the cervical area and get the texture features of the area on the CT Scan image data. Segmentation algorithms we used are edge detection and region based snake model. The feature extraction process is in form of Gray Level Co occurrence Matrix GLCM texture analysis with machine learning in the form of Support Vector Machine SVM . The results suggest that SVM can be used to classify images to normal and abnormal groups with a sensitivity of 95,2 , a specificity of 90,5 , an accuracy of 92,9 , a precision of 90,9 and an overall error of 7,1 .

Abstrak :
Pemeriksaan yang paling tepat dalam menentukan volume jaringan lemak visceral dilakukan dengan menggunakan modalitas CT-scan. Namun karena setiap slice citra memiliki bentuk dan lokasi lemak visceral yang berbeda-beda, maka penentuan volume menjadi tidak mudah. Sehingga, Computer Aided Diagnosis (CAD) dapat dijadikan salah satu solusi untuk membantu tenaga ahli dalam pembacaan citra dan menganalisa area lemak terutama area lemak visceral pada citra abdomen dengan lebih akurat. Dalam penelitian ini, sistem CAD dikembangkan dengan menggunakan metode segmentasi Thresholding, ekstrasi ciri berbasis Gray Level Co-Occurrence Matrix (GLCM) dan klasifikasi citra lemak visceral menggunakan Multilayer Perceptron (MLP). Penelitian ini mengolah data 665 citra CT-scan abdomen dari 38 pasien yang diperoleh dari Rumah Sakit Persahabatan Jakarta. Data tersebut dibagi menjadi 70% citra sebagai data pelatihan dan 30% citra sebagai data pengujian. Hasil performa sistem CAD yang direpresentasikan sebagai tingkat keakurasian dengan nilai sebesar 98.73% untuk data pelatihan dan 95.58% untuk data pengujian. Selain itu, juga diperoleh informasi bahwa hasil kalkulasi volume area jaringan lemak visceral dengan nilai terbesar yaitu sebesar 1238.89 dengan tebal slice sebesar 5 mm. Sedangkan ketebalan 10 mm diperoleh volume sebesar 1072.91 Sementara untuk hasil kalkulasi volume area jaringan lemak visceral terkecil sebesar 107.57 pada ketebalan 5 mm. Sedangkan ketebalan 10 mm diperoleh volume sebesar 47.43 . Evaluasi pada proses segmentasi dilakukan menggunakan metode SSIM dengan mengahasilkan nilai rata-rata SSIM untuk keseluruhan data sebesar 0.843 pada data latih dan 0.838 pada data uji. Dari hasil penelitian ini, sistem CAD berhasil dikembangkan untuk membantu dalam proses mengestimasi volume area jaringan lemak visceral. Namun, tingkat keakurasian antara kalkulasi volume lemak visceral menggunakan sistem CAD dan software CT-scan belum dapat diperoleh dengan baik. ......The most precise examination in determining the volume of abdominal fat tissue is using a CT-scan modality. However, because each slice image has a different shape and location of visceral fat, it is not easy to determine the volume. So that, Computer Aided Diagnosis (CAD) can be used as a solution to assist experts in reading images and analyzing fat areas, especially visceral fat areas on abdominal images more accurate. In this study, a CAD system was developed using the Thresholding segmentation method, feature extraction based on Gray Level Co-Occurrence Matrix (GLCM) for the identification of abdominal fat. Next, in the classification process, the visceral fat area is separated from the subcutaneous fat area using Multilayer Perceptron (MLP). This study processed data from 665 abdominal CT-scan images from 38 patients obtained from Persahabatan Hospital. The data is divided into 70% images as training data and 30% images as test data. The results of the CAD system performance are represented as the level of accuracy with a value of 98.73% for training data and 95.58% for test data. In addition, information was also obtained that the calculation of the volume of visceral fat tissue areas with the largest value of 1238.89 with a slice thickness of 5 mm. While the thickness of 10 mm obtained a volume of 1072.91 Calculation of the volume of the volume area of ​​the smallest visceral fat tissue of 107.57 at 5 mm thickness. While the thickness of 10 mm obtained a volume of 47.43 . Evaluation of the segmentation process was carried out using the SSIM method by producing an average SSIM value for the entire data of 0.843 in the training data and 0.838 in the test data. From the results of this study, a CAD system was successfully developed to assist in the process of estimating the volume of visceral fat tissue area. However, the level of accuracy between the calculation of visceral fat volume using CAD systems and CT-scan software has not been obtained properly.

Abstrak :
Modalitas pencitraan yang sering digunakan pada diagnosis kanker kandung kemih adalah Computed Tomography (CT). Informasi dari hasil pembacaan citra CT diharapkan berupa volume pada jaringan abnormal yang berguna untuk penentuan tindakan medis selanjutnya. Namun karena pada setiap slice citra memiliki ukuran, bentuk dan lokasi kanker kandung kemih yang berbeda-beda, maka penentuan volume menjadi tidak mudah. Oleh karena itu untuk meningkatkan keakuratan dan konsistensi penentuan diagnosa dan volume jaringan abnormalnya maka diperlukan bantuan Computer-Aided Diagnosis (CAD). CAD dapat dikembangkan menjadi perhitungan volume jaringan abnormal berdasarkan segmentasi dan klasifikasi citra. Pada penelitian ini, sistem CAD yang dikembangkan menggunakan metode segmentasi, fitur ekstrasi berbasis Gray Level Co-Occurrence Matrix (GLCM) dan klasifikasi citra normal dan abnormal menggunakan k-Nearest Neighbors (kNN). Data yang digunakan pada penelitian ini adalah 300 citra CT kandung kemih dari Rumah Sakit Kanker Dharmais, terdiri dari 100 citra normal dan 200 citra abnormal dengan 210 citra digunakan sebagai data pelatihan dan 90 citra digunakan sebagai data pengujian. Hasil performa sistem klasifikasi citra berupa akurasi sebesar 94,28% untuk data pelatihan dan 91,22% untuk data pengujian. Pada penelitian ini dilakukan kalkulasi volume jaringan abnormal kandung kemih terhadap 6 pasien dan hasilnya diperoleh volume terkecil 4,15 cm³ dan terbesar 77,40 cm³. Selain itu ditunjukkan pula volume jaringan abnormal terkecil yang dapat dideteksi adalah sekitar 0,03 cm³. ......The most frequency using in the diagnosis of bladder cancer is computed tomography (CT). Information from CT image reading is expected to be in in the form abnormal tissue volume that is useful for determining the next treatment. However, the resulting image slices has a different size, shape and location of bladder cancer, determining the volume is not easy. Therefore, to improve the accuracy and consistency of reading medical images and abnormal tissue volume, Computer-Aided Diagnosis (CAD) can be assisted. CAD can be developed into abnormal tissue volume calculations based on image segmentation and classification. In this study, the CAD system was developed using preprocessing, segmentation, feature extraction based on Gray Level Co-Occurrence Matrix (GLCM) and normal and abnormal image classification using k-Nearest Neighbors (kNN). The data used in this study are 300 bladder CT images from Dharmais National Cancer Hospital, consisting of 100 normal images and 200 abnormal images. 210 images are used as training data, and 90 images are used as testing data. The results of CAD system performance in this study are in the form of the accuracy of 94.28% for training data and 91.22% for testing data. In this study, the volume of abnormal bladder tissue was calculated for 6 patients, and the results obtained the smallest volume is 4.15 cm³ and the largest 77.40 cm³. In addition, it is also shown that the smallest abnormal tissue slice in slice volume that can be detected is about 0.03 cm³.